Bibliographic record
Abstract
In order to supplement the special issue while opening up to other fields aside literature, here are six short presentations of data bases that were devised during historical or sociological researches on arts and culture. The authors were asked six questions : on the birth of the data basis project, the body of documents, the scientific field and/or theoretical framework, the softwares used, the scientific results reached thanks to this specific approach, as well as the accessibility of the data bases produced. The data bases presented were selected on the grounds of the pioneering character, rigour and breadth of the research projects: the data basis on writers, works, and the French-speaking Belgian journals on literature of the interuniversity literary studies collective (Collectif interuniversitaire d’étude littéraire — CIEL) ; the data basis that served for the publishing of Pierre Bayle’s letters ; the Chronopéra data basis ; the Manart data basis dedicated to artistic and literary 19 c. events ; the data bases in the book and publishing history set up by the research group on book studies in Québec (Groupe de recherches et d’études sur le livre au Québec - GRÉLQ) ; the ARTL@S project that includes in particular a data basis of exhibition catalogues.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.021 | 0.036 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.106 | 0.043 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".