ChiSCor: Children's Story Corpus
Bibliographic record
Abstract
<p>ChiSCor is a new corpus containing 619 fantasy stories, told freely by 442 Dutch children aged 4-12. ChiSCor was compiled for studying how children render character perspectives, and unravelling language and cognition in development, with computational tools. ChiSCor hosts text, audio, and annotations for character complexity and linguistic complexity. Additional metadata (e.g. education of caregivers) is available for one third of the Dutch children. ChiSCor also includes a small set of 62 English stories for which the same kinds of annotations are available, as well as detailed background information for a smaller subset of English-speaking children. </p> <p>This is the corpus accompanying the publication "ChiSCor: A Corpus of Freely Told Fantasy Stories by Dutch Children for Computational Linguistics and Cognitive Science", presented at the Conference for Natural Language Learning (CoNLL) 2023 in Singapore. <p>Link to the paper accompanying ChiSCor: <a href="https://aclanthology.org/2023.conll-1.23/">https://aclanthology.org/2023.conll-1.23/</a></p> <p>Authors: <b>Bram M.A. van Dijk*, Max J. van Duijn*, Suzan Verberne, Marco R. Spruit</b></p> <p><em>* indicates equal contribution </em></p> <p><b>Note: if you use this corpus, please cite the paper mentioned above!</b></p> <p><b>Note: read the corpus manual, also if you are looking for a quick overview of ChiSCor's contents.</b></p> <p><b>Note: the corpus (structure) is best viewed in the 'tree view' mode.</b></p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.077 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".