Bibliographic record
Abstract
Peer review is a mainstay of scientific publishing and, while peer reviewers and scientists report satisfaction with the process, peer review has not been without criticism. Within this editorial, the peer review process at the IJTMB is defined and explained. Further, seven steps are identified by the editors as a way to improve efficiency of the peer review and publication process. Those seven steps are: 1) Ask authors to submit possible reviewers; 2) Ask reviewers to update profiles; 3) Ask reviewers to "refer a friend"; 4) Thank reviewers regularly; 5) Ask published authors to review for the Journal; 6) Reduce the length of time to accept peer review invitation; and 7) Reduce requested time to complete peer review. We believe these small requests and changes can have a big effect on the quality of reviews and speed in which manuscripts are published. This manuscript will present instructions for completing peer review profiles. Finally, we more formally recognize and thank peer reviewers from 2018-2020.
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 | 0.161 | 0.519 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.037 | 0.018 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.044 | 0.074 |
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".