Manuscript processing times are negatively correlated with journal impact factors
Bibliographic record
Abstract
A limited two-year time window is used to calculate a journal's impact fac- tor, which suggests that journals with faster publication times will have higher impact factors. To confirm this hypothesis, the manuscript processing times (median time to acceptance and median time to publication) were determined for articles from 42 journals selected from seven different research areas. Both accep- tance time and publication time were found to be negatively correlated with impact factor. When analysed by research category, processing times were even more strongly negatively correlated with the group's median impact factor. Resume : Le calcul du facteur d'impact se fait a partir d'une intervalle limitee de deux ans, ce qui donne a penser que les revues scientifiques dont les delais de publi- cation sont plus courtes seront aussi celles qui jouiront des facteurs d'impact les plus eleves. Pour confirmer cette hypothese, nous avons determine les delais de traite- ment des manuscrits (delai moyen d'acceptation et delai moyen de publication) pour les articles de 42 revues selectionnees parmi sept domaines de recherche diffe- rents. Nous avons constate une correlation negative entre les deux delais d'accepta- tion et de publication et le facteur d'impact. Lors de l'analyse par domaine de recherche, nous avons constate une correlation encore plus fortement negative entre les delais de traitement et le facteur d'impact du groupe moyen.
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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 | 0.014 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".