Bibliographic record
Abstract
ABSTRACT Data are becoming an essential product of scholarship, complementing the roles of journal articles, papers, and books. Research data can be reused to ask new questions, to replicate studies, and to verify research findings. Data become even more valuable when linked to publications and other related resources to form a value chain. Types and uses of data vary widely between disciplines, as do the online availability of publications and the incentives of scholars to publish their data. Publishers, scholars, and librarians each have roles to play in constructing a new scholarly information infrastructure for e‐research. Technical, policy, and institutional components are maturing; the next steps are to integrate them into a coherent whole. Achieving a critical mass of datasets in public repositories, with links to and from publisher databases, is the most promising solution to maintaining and sustaining the scholarly record in digital form.
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 | 0.077 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.026 | 0.070 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.054 | 0.044 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".