Bibliographic record
Abstract
This is the 3 rd issue of 2024.We are completing the first year of the JINT/JIRS transition into an Open Access peer review-based publication.The first observations are mixed.i.)The impact factor (IF) remains steady: The 2023 IF is 3.1, slightly lower than the 2022 IF, and the five-year, updated in 2023, is 3.2.However, it is expected that it will increase in 2024.ii.)The number of submissions has declined, but this was expected due to the transition.iii.)Our journal is very respected, and the quality of published papers is very high!Our central objective remains the same: continue seeking ways to further improve the journal and enhance its reputation.At this point, once more, and I certainly hope that it will be for the last time, I do want to reiterate the following very important issues that will expedite and facilitate the paper review process.The below points are presented to help you, the authors, and to provide you with a just review of your contribution.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.020 | 0.014 |
| Insufficient payload (model declined to judge) | 0.051 | 0.034 |
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".