A new approach to data access and research transparency (DART)
Bibliographic record
Abstract
Recent debates on transparency and replicability suggest that JIBS needs to update its approach on data access and research transparency (DART). We propose a series of initiatives, knowing well that there is a balance to be struck. There are clear benefits on the one hand, chief among these the potential for learning and knowledge accumulation, and equally manifest challenges on the other: the imperative to respect privacy, confidentiality, and intellectual property rights. Without addressing these challenges, will there be the high-quality data on which the benefits depend? We present access and transparency objectives, and set out how an actionable and effective approach towards DART will be implemented, but also address ethical, legal, and organizational challenges of concern to us as a scholarly community.
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 | 0.481 | 0.498 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.015 | 0.089 |
| Scholarly communication | 0.060 | 0.089 |
| Open science | 0.012 | 0.046 |
| Research integrity | 0.025 | 0.050 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier 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".