impact/holdout.jsonl

{"input": "The expected payout is estimated using the APHRODITE dataset, for the period 1961–2004.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turkish Labour Force Survey to assess the impact on Turkish employment and wages.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "the most recent (2007-2011) statistics in the World Bank ’ s Edstats data reveal that female literacy is less than 60 percent, on average.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Authors ’ presentation based on UNHCR Global Trends 2013 (UNHCR 2014).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "To construct such estimate, we use the 1995 / 96 NLSS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we use again information provided by the Penn World Table and World Bank databases.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The valuation studies by Tol (2007) and Anthoff et al. (2010) are also based on wetland losses derived from the Global Vulnerability Analysis (Hoozemans et al. 1993).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The exposure of an individual in the JLMPS 2010 sample to the supply of public schooling is then determined by the number of sex-appropriate basic (or secondary) public schools (per 1, 000 individuals) that were available to them in their subdistrict of birth at the time they were of age to enroll in that school level (six years of age for basic and 15 years for secondary).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Deloitte own elaboration based on Statistics Poland and ZUS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "This paper combines district-level government spending data from Indonesia and natural disaster damage indices to analyze the extent to which districts are forced to reallocate their expenditures across categories after the incidence of floods, earthquakes, and volcanic eruptions.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "we provide some summary statistics from our main source of data, the UNHCR Population Statistics Database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "This is consistent with the national figures from the 2007 DHS survey, which found labor force participation rates of 34 % for women aged 15-19 and 49 % for women ages 20-24.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "the World Bank’s Global Monitoring Database offers several poverty estimates, which are all derived from the latest available Living Standards Measurement Survey (LSMS) for the respective country.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "For the purpose of this analysis, we use the TLSS 2007 dataset and the HRVD dataset contained in the CAVR data publication.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We have used the FBS Census of Private Schools (PEIP, 2000) to guide our fieldwork and feasibility study for LEAPS and found it tallied with the situation on the ground quite well, even in remote villages.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "according to SESRE data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Data from this survey is presented under each of the six pathways in this document.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The Iraq Crisis Response Study [ongoing] will assess the impact of the Islamic State and oil price-related crises on IDPs and households left behind in IS controlled areas.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "For the purpose of this analysis, we use the TLSS 2007 dataset and the HRVD dataset contained in the CAVR data publication.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Using detailed data on expenditures from a 2017/18 household budget survey and caloric information from the Brazilian Table of Food Composition, calorie intake is assigned to more than 1,400 items to estimate the cost per calorie for a representative group of the population.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We compute this index for each cluster at the time of each Afrobarometer survey to assess how refugee-induced changes in diversity differ from standard indices of diversity.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Authors’ analysis based on data from BASIX and APHRODITE.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "In 2012, average inflows in the World Bank dataset were over 9. 5 billion USD and over 3 billion USD in the OECD data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We now turn to Google's Community Mobility Reports, our second source of information on the intensity of pandemic-related disruptions in Mali.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["report"]}]}}
{"input": "Probability of having a household member abroad was estimated using a Linear Probability model and ALCS 2013–14 data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Data from the Demographic and Health survey shows that more than 40 percent of adult women have no education, compared to fewer than 20 percent of men, while 23 percent of women and 44 percent of men have some secondary schooling (DHS 2007).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The survey was used to draw a profile for skills and potential opportunities for refugees and host", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The indicator will track the percentage of administered COVID-19 vaccines which are captured in the national vaccination digital registry.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["monitoring"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["administrative"]}]}}
{"input": "according to the Liberian labor force survey from 2010, the ratio of girls to boys enrolled in primary school has risen from 72 in 2000 to 90 in 2009.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Of the 3, 500 sources detailed in the overarching UN Global Migration Database, 1, 107 were suitable for analysis, once repeated censuses had been removed or combined.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "use two complementary geocoded household data sets to analyze outcomes in Ghana: the Demographic and Health Survey (DHS) and the Ghana Living Standard Survey (GLSS), which provide information on a wide range of welfare outcomes.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "The paper makes use a new dataset called ACLED (Armed Conflict Location and Events Dataset) to allow for this type of disaggregated analysis.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "ACLED is designed to parse out both the temporal and spatial actions of rebels and governments within civil wars.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "as per the SEIS survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Similar results can be gleaned from the Multi-Sector Needs Assessment Poland 2023 survey results, which show that slightly more than 5% of respondent households receive income from selfemployment or similar activities.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "the household survey was designed in view of combining it with the nationally representative 1998 Vietnam Living Standards Survey (VLSS) to predict baseline consumption expenditures for SIRRV households", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "To construct such estimate, we use the 1995 / 96 NLSS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "To construct such estimate, we use the 1995 / 96 NLSS data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we collapse the DHS mining data at the district level.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "This other World Bank study overlays the same GLOFRIS flood maps as above with geo-localized household surveys (using the Demographic and Health Surveys [8] ) to assess the exposure of poor people to river floods relative to the exposure of non-poor people.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["needs_evaluation"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Deloitte worked with disaggregated household-level data, ensuring comparability (converting all Ukrainian hryvnia incomes into Polish zloty based on daily exchange rates in the time of the interview, and 3-month into 1-month remittance incomes).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "Estimates from the LEAPS census show that as a percentage of enrolled children, the numbers in two of the three districts are slightly higher than those of the population census.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "Data from the Demographic and Health survey shows that more than 40 percent of adult women have no education, compared to fewer than 20 percent of men, while 23 percent of women and 44 percent of men have some secondary schooling (DHS 2007).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Tol 2007 drew on the Global Vulnerability Analysis and other country studies for quantification of wetland loss from 1 m SLR for a global analysis", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We have computed the per person, per month in-kind aid quantities into annual values using the same prices as other food items based on SESRE data, mapping them to the closest food item in SESRE (this was not straightforward as the items are different).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Source: World Bank Staff based on SESRE 2023.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The Multidimensional Poverty Index (MPI) explores this multiple deprivation, capturing differences across three dimensions of well-being: health, education, and", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "We use consumption and price data from the National Risk and Vulnerability Assessment (NRVA) 2007/08, conducted by the Government of Afghanistan Central Statistics Organization and the Ministry of Rural Rehabilitation and Development.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The population centers are from the GRUMP settlement points dataset (CIESIN et al. 2004a) and the World Gazetteer database (Helders 2005). Urban boundaries are from the GRUMP urban extents database (CIESIN et al. 2004b).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["geospatial"]}]}}
{"input": "In the present analysis, we use 2, 530 of these.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we again use LEDA21 to link data on ethnicity from Murdock ’ s Atlas with data on ethnicity from the EPR-ER dataset and, later, with data from Afrobarometer.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "This article utilizes the mobile Vulnerability and Assessment Mapping Survey (mVAM)", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "Incorporation of controls from our cyclone panel database may permit more accurate estimation of the timing and magnitude of responses to these", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The 3B42RT daily derived product is what is used in this paper.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Material from the German 2005 micro-census was therefore used to supplement the data for Germany (see appendix 3).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "we use information from the COVID-19 phone panel survey, our third source of information used to investigate the intensity of pandemic-related disruptions within Mali.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "According to the 2013 Gallup Poll, 90 percent of respondents in Lebanon agreed with the statement that knowing people in high positions is critical to getting a job.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "For the purpose of this analysis, we use the TLSS 2007 dataset and the HRVD dataset contained in the CAVR data publication.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we have constructed what we have called an ‘ ad hoc panel ’, whereby we exploit the time-variation of the variables of interests (age, attendance status and grade attained by the respondents) by reshaping the cross-sectional structure of the TLSS 2001 dataset.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "the adapted approach simulates the travel trajectories based on the multimodal transportation model and accounts for public transport waiting times, road speed limits, and designated origin and destination pairs acquired from the JICA commuter travel survey.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we use data on the precise locations of refugee camps, their yearly size, and — most importantly — their annual composition in terms of countries of origin.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "we use again information provided by the Penn World Table and World Bank databases.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Using the NLSS data we begin by estimating a regression of the form: yk s = δs + α (ak s − a) + βs (Ek s − Es) + χs (Hk s − Hs) + vk s (4)", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Authors ’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "In our analysis, we use Afrobarometer, EPR, and Murdock Atlas data; therefore, we can use LEDA functions to link the different ethnic groups to each other.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["survey"]}]}}
{"input": "The ACLED data for the Central African conflicts were aggregated up to the 8. 6x8. 6km squares and merged with information on other explanatory variables aggregated to the same level.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The UNEP/Risoe data reports total CERs by 2012 and 2020.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We have used the FBS Census of Private Schools (PEIP, 2000) to guide our fieldwork and feasibility study for LEAPS and found it tallied with the situation on the ground quite well, even in remote villages.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["policy_informing"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "we provide some summary statistics from our main source of data, the UNHCR Population Statistics Database.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["database"]}]}}
{"input": "In robustness checks we also make use of the Standardized Precipitation Evapotranspiration Index (SPEI) (Vicente-Serrano, et al., 2010) that integrates evapotranspiration into the standard precipitation index.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["indicator"]}]}}
{"input": "in complement to routine data through DHIS2, in support of the country’s overall HMIS.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["background"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Third Party Monitor / PMU Responsible for Monitoring; Measures subcomponent 1.1 Under UNICEF", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["monitoring"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "We test the two parts of the redistribution hypothesis using the most recent data from Luxembourg Income Study for 20 OECD countries covering the period 1967-2005 (total number of country/years is 110).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["supporting"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["census"]}]}}
{"input": "we additionally access a second source of administrative data stemming from the WHO's COVID19 vaccination dashboard (WHO 2020b).", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "Combined with the Ethnic Power Relations- Ethnicity of Refugees 2019 dataset, we are able to predict changes in ethnic diversity induced by refugee inflows.", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["quantitative_analysis"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}
{"input": "we again use LEDA21 to link data on ethnicity from Murdock ’ s Atlas with data on ethnicity from the EPR-ER dataset", "output": {"classifications": [{"task": "purpose_action", "labels": ["quantitative_analysis", "needs_evaluation", "policy_informing", "monitoring", "contextual_reference"], "true_label": ["contextual_reference"]}, {"task": "usage", "labels": ["primary", "supporting", "background"], "true_label": ["primary"]}, {"task": "typology", "labels": ["survey", "census", "database", "administrative", "indicator", "geospatial", "microdata", "report", "estimates", "other"], "true_label": ["other"]}]}}