README.md

metadata

pretty_name: 'SchLAIMS: A Dataset for Claim Selection in Scholarly Debates'
language:
  - en
license: other
task_categories:
  - text-classification
size_categories:
  - 10K<n<100K
configs:
  - config_name: full
    data_files:
      - split: train
        path: data/full/train.parquet
      - split: validation
        path: data/full/dev.parquet
      - split: test
        path: data/full/test.parquet
  - config_name: majority
    data_files:
      - split: train
        path: data/majority/train.parquet
      - split: validation
        path: data/majority/dev.parquet
      - split: test
        path: data/majority/test.parquet

SchLAIMS: A Dataset for Claim Selection in Scholarly Debates

What It Is

SchLAIMS is a dataset for paragraph-level claim selection. Given one scientific paragraph, the task is to select the one sentence that states the paragraph's central point, or return null when the paragraph makes no single claim.

The release contains 500 linked article, comment, and reply sets found through OpenAlex: 29,703 paragraphs and 114,138 sentences. In the files, those three document turns are named original, critique, and response.

What A Label Means

A claim is the sentence that states the central point the author wants the reader to accept. Evidence, methods, citations, details, qualifications, and caveats usually serve as premises supporting that point. Annotators read the whole paragraph, choose its highest-level takeaway, and do not force a claim when no sentence clearly plays that role. Each valid label is therefore one source sentence_n or null.

One row contains a paragraph, its ordered sentences, its selected claim, its document turn, and IDs that link it back to its triad and source document.

Example

This unanimous training example is recoverable in either configuration:

• triad_name: triad_4498
• triad_id: W3135045181_W4283257629_W4283256895
• turn: original
• paragraph_id: triad_4498__original__p0037
• paragraph_index: 37
• OpenAlex work: W3135045181
• Source: Laroche and L'Esperance (2021), Cancer Incidence and Mortality among Firefighters: An Overview of Epidemiologic Systematic Reviews, DOI 10.3390/ijerph18052519 (CC BY)

1. The extent to which a systematic review can draw conclusions about the effects of an intervention depends on the validity of the data and results from the included studies.

2. In particular, a meta-analysis of invalid or low-quality studies may produce a misleading result, yielding a narrow confidence interval around the wrong intervention effect estimate [16].

3. Variations in study quality can explain differences in the findings of studies that are included in a systematic review.

4. As a result, the quality of a study will affect the strength of the evidence that can be drawn from it.

5. In other words, it determines whether we can be confident that the results of a study reflect the 'truth' and by extrapolation, whether we can be confident in the results of the systematic review [16,17].

The selected claim is sentence 1. All three label passes selected it. The alternative would be null if none of the five sentences expressed one clear central claim.

How Labels Were Made

These are AI-consensus reference labels, not human-adjudicated gold labels. We used agents to create the training labels only after a hidden test showed that they could follow the human-written guidelines and usually select the same claims as human annotators. The agents never saw the human answers. Across 10 seeded low-reasoning GPT-5.4 mini runs, the retained human-consensus test had about 0.84 mean claim F1 and 0.948 mean sentence accuracy (161 sentences and 27 claims). This validates the labeling method; it does not mean every released paragraph was checked by a person. Details are in the SchLAIMS validation pipeline.

The release applies revised long-block paragraphing, scispaCy sentence segmentation, and three independent GPT label passes. An exact choice made by at least two passes becomes the consensus label. The full view retains all paragraphs and deterministically selects one observed vote for each of 337 three-way disagreements. The majority view excludes those disagreements. It is a different, less ambiguous population, not simply a better benchmark.

Model Results And Human Validation

The released Longformer selector has two jointly reported fixed-test results. It was also checked on the retained human-consensus paragraphs after removing one paragraph found verbatim in its training data.

Reference labels	Population	Paragraphs	Precision	Recall	Claim F1
AI consensus, full test	Fixed test	4,603	0.7032	0.7039	0.7035
AI consensus, majority-only test	Fixed test	4,538	0.7117	0.7106	0.7111
Human consensus	External validation	26	0.7692	0.7692	0.7692

The human row is a small external validation check, not a third canonical test benchmark. Its source table, exclusions, overlap audit, and hashes are provided as auxiliary validation provenance. The complete 34-paragraph diagnostic is not a reported model result.

Splits And Loading

The split contains 350 training, 75 development, and 75 test triads. Linked article, comment, and reply documents always stay together.

from datasets import load_dataset

full = load_dataset("danielscottsmith/schlaims", "full")
majority = load_dataset("danielscottsmith/schlaims", "majority")

Native Parquet and JSONL files support ordinary use. The three hashed archives preserve raw sources, exact processed data, and label provenance for reproduction.

Uses And Limits

The dataset supports training and evaluating paragraph-level claim selectors, studying argumentative structure, and assisting scientific-text annotation. It is not fact checking, evidence-quality grading, importance ranking, extraction of every possible claim, or isolated-sentence classification. These 500 triads do not represent science as a whole.

Rights And Citation

Source prose remains under its original publisher terms and is redistributed under the PI-approved basis for this release. OpenAlex's CC0 metadata license does not relicense article text. Project-created labels, boundaries, audits, and manifests are available under CC BY 4.0.

@dataset{smith_schlaims_2026,
  author  = {Smith, D. S. and Verdi, D. A. and Chen, R. and Zhang, H. and McFarland, D. A.},
  title   = {SchLAIMS: A Dataset for Claim Selection in Scholarly Debates},
  year    = {2026},
  version = {1},
  url     = {https://huggingface.co/datasets/danielscottsmith/schlaims}
}

Please also cite OpenAlex: Priem et al. (2022), "OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts," arXiv:2205.01833.