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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+KenyaESG_train_environmental.xlsx filter=lfs diff=lfs merge=lfs -text
+KenyaESG_train_governance.xlsx filter=lfs diff=lfs merge=lfs -text
+KenyaESG_train_social.xlsx filter=lfs diff=lfs merge=lfs -text

KenyaESG_correction_log.txt

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+KenyaESG — CORRECTION LOG (post-v3)
+====================================
+Scope: correction of false-positive [COMPANY] masks only. No rows removed;
+3,900 sentences per pillar preserved. Labels and [PERSON] masks unchanged.
+Swahili and OCR-degraded sentences were retained as-is by decision.
+
+False-positive [COMPANY] restored to the original common word, by context:
+  "Modern Slavery Business [COMPANY]"      -> "...Business Standard"
+  "Diversity,[COMPANY] & inclusion"        -> "Diversity, Equity & inclusion"
+  "employment [COMPANY]"                   -> "employment equity"
+  "gender [COMPANY]" / "Gender [COMPANY]"  -> "gender equity" / "Gender equity"
+  "Promoting gender [COMPANY]"             -> "Promoting gender equity"
+  "any [COMPANY] incentive plan"           -> "any equity incentive plan"
+  "the Dutch [COMPANY]"                    -> "the Dutch Standard"
+  "[COMPANY] wa dunia"                     -> "uchumi wa dunia"
+  "protect biodiversity [COMPANY]"         -> "protect biodiversity Standard"
+
+Corrections applied: env 6, soc 16, gov 7 (total 29).
+[COMPANY] token count: env 364->358, soc 443->427, gov 488->481.
+Row counts unchanged: 3,900 per pillar. Label sums unchanged.
+
+Evaluation sets: no named entity detected; existing [COMPANY]/[PERSON] masks
+present (env 11, soc 20, gov 6 lines). Released unchanged.
+
+Note: the corpus retains a small number of Swahili and OCR-degraded sentences
+(~18-20 Swahili and ~30-48 OCR per pillar), by design, reflecting the original
+NSE reports. It is therefore not strictly English-only.

KenyaESG_train_environmental.xlsx

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KenyaESG_train_governance.xlsx

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KenyaESG_train_social.xlsx

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README.md

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 ---
 license: apache-2.0
+language:
+- en
+- sw
+task_categories:
+- text-classification
+tags:
+- esg
+- esg-disclosure
+- sustainability
+- finance
+- nairobi-securities-exchange
+- kenya
+- roberta
+pretty_name: KenyaESG
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: environmental
+  data_files:
+  - split: train
+    path: train_environmental.csv
+  - split: test
+    path: eval_environmental.csv
+- config_name: social
+  data_files:
+  - split: train
+    path: train_social.csv
+  - split: test
+    path: eval_social.csv
+- config_name: governance
+  data_files:
+  - split: train
+    path: train_governance.csv
+  - split: test
+    path: eval_governance.csv
 ---
+
+# KenyaESG: Sentence-level ESG Disclosure Classification Dataset (NSE)
+
+Sentence-level dataset for three independent binary ESG classification tasks (Environmental, Social, Governance) built from corporate reports of firms listed on the Nairobi Securities Exchange (NSE), 2010–2024, combined with reference sentences from Schimanski et al. (2024). Companion data to the fine-tuned classifiers `josephagossa/KenyaESG-RoBERTa-{env,soc,gov}`.
+
+## Structure
+
+Three configurations, one per pillar, each with a `train` and a `test` split:
+
+```python
+from datasets import load_dataset
+ds = load_dataset("josephagossa/KenyaESG", "environmental")   # or "social", "governance"
+ds["train"]   # 3,900 sentences
+ds["test"]    # 100 human-annotated sentences
+```
+
+| Config | train | test |
+|---|---|---|
+| environmental | 3,900 | 100 |
+| social | 3,900 | 100 |
+| governance | 3,900 | 100 |
+
+The train and test sentences are disjoint (the 100 evaluation sentences per pillar were removed from training to prevent leakage). Together they reconstruct the full 4,000-sentence pool per pillar. Because the three pillars are modelled as independent binary tasks, a sentence may be positive on more than one pillar.
+
+## Fields
+
+**train** splits: `id`, `text`, `label` (0/1 for the pillar), `source`.
+**test** splits: `id`, `text`, `annotator_A`, `annotator_B` (two independent binary human judgments). Inter-annotator agreement (Cohen's κ): 0.94 (environmental), 0.86 (social), 0.88 (governance). Both annotations are released rather than a single adjudicated label, so users can recompute agreement and adjudicate themselves; the three evaluation samples are drawn independently per pillar (the sentences differ across pillars).
+
+## Companion models (performance vs. human benchmark)
+
+| Model | Pillar | F1 | DOI |
+|---|---|---|---|
+| KenyaESG-RoBERTa-env | Environmental | 0.917 | 10.57967/hf/9126 |
+| KenyaESG-RoBERTa-soc | Social | 0.882 | 10.57967/hf/9127 |
+| KenyaESG-RoBERTa-gov | Governance | 0.916 | 10.57967/hf/9128 |
+
+## Sources
+
+The `source` field distinguishes two origins. **Kenya**: sentences from annual, integrated, and sustainability reports of NSE-listed firms (2010–2024); training labels were assigned by a keyword-based filter and refined by a single-reviewer pass (not full manual annotation). **Schimanski et al. (2024)**: reference sentences from "Bridging the gap in ESG measurement" (*Finance Research Letters* 61, 104979), available under Apache-2.0.
+
+## Privacy and anonymization
+
+Company and personal names are masked in the `text` field (`[COMPANY]`, `[PERSON]`) in line with the anonymisation principles of the Kenya Data Protection Act, 2019 (No. 24 of 2019) and Regulation (EU) 2016/679 (GDPR). Masking of personal names follows these principles; company names (from public filings) are masked as a precaution. Masking is semi-automatic and not guaranteed exhaustive; a small number of false-positive masks on common words (e.g. "equity", "equality", "Standard") were subsequently restored — see `correction_log.txt`. The dataset must not be used to attempt re-identification of individuals or firms.
+
+## Intended use and limitations
+
+Intended for training and evaluating sentence-level ESG text classifiers. The training labels for the Kenya portion are keyword-derived and single-reviewer-checked; the test splits are the appropriate benchmark for honest performance estimates. The dataset captures disclosure *intensity*, not disclosure *quality*; sample sizes are modest. The corpus is predominantly English but retains a small number of Swahili and OCR-degraded sentences, reflecting the original NSE reports.
+
+## License and citation
+
+© 2026 Joseph Agossa. The dataset compilation, structure, and annotations are licensed under Apache-2.0; the underlying verbatim report text remains under the copyright of the respective issuing companies and is included for research purposes under a fair-dealing rationale, with sources documented.
+
+Archived on Zenodo (concept DOI, resolving to the latest version): https://doi.org/10.5281/zenodo.20608236
+
+```bibtex
+@misc{agossa2026kenyaesg,
+  author    = {Agossa, Joseph},
+  title     = {KenyaESG: A Sentence-level ESG Disclosure Classification Dataset
+               for Nairobi Securities Exchange Corporate Reports},
+  year      = {2026},
+  publisher = {Zenodo},
+  doi       = {10.5281/zenodo.20608236}
+}
+```
+
+Working paper: Agossa, J. (2026). *Pricing the Cost of Compliance: Equity Reactions to Mandatory ESG Disclosure in Kenya.* [SSRN ID to be added].
+
+Schema adapted from Schimanski, T., Reding, A., Reding, N., Bingler, J., Kraus, M., & Leippold, M. (2024). Bridging the gap in ESG measurement. *Finance Research Letters*, 61, 104979.