Bibliographic record
Abstract
As the readers of this journal know all too well, conducting a study takes a lot of time, energy, commitment, and, most of all, work. I do not have to list the myriad steps that need to be taken, often with delays in between, to take a study from good idea to analysis. There are often sweat and tears, if not blood, and, at the end of the process, there may be exhaustion. Then the piles of paper and data-rich files languish on a desk or computer, never to see the light of day. Or, perhaps, a manuscript is written and submitted and then rejected or needs extensive revision. And it languishes again. Many of us have been in this position and remember what that rejection felt like and how hard it was to revise and submit again, or not. .
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 | 0.051 | 0.329 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.018 | 0.055 |
| Insufficient payload (model declined to judge) | 0.043 | 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".