Bibliographic record
Abstract
At its best, peer review can mean receiving constructive feedback to help you make the most of your writing. At the Health Information and Libraries Journal, we strive to make the peer review a positive process for both authors and referees. We adopt a process of double-blind peer review. To receive two reviews in a timely manner, three referees are initially invited for each article submitted. The referees are asked to submit their review noting errors, areas of ambiguity or clarification required before the editor and editorial team consider the manuscript ready for publication. As with most journals, it's unlikely that your writing will be accepted in its original form; a typical outcome will be for a recommendation for major or minor revisions. This is good! It means the editorial team has seen something of likely interest to their readership and wants to help you develop it to a publishable standard. There can be a surprising amount of development and change in a manuscript from original submission through to publication. While you may be experienced in your field, you may not have much experience of writing for publication. As a referee, you get an intriguing insight into the shape of manuscripts in their original 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 | 0.020 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.034 | 0.041 |
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".