Bibliographic record
Abstract
Writing an editorial to reflect on the achievements and challenges of the Journal of Physical Activity and Health (JPAH) the past year is an interesting exercise.Upon receiving publication statistics from the publisher, I tried to understand why some things went just as planned, and others did not go that well.Our commitment of going global, for example, is a case of success.In 2022, 100 articles were published by authors from 54 different countries.In fact, we received submissions from 65 countries.This result represents a major shift in the dynamics of research in physical activity and health worldwide.In 2012, Pratt et al 1 identified a mismatch between where the studies on physical activity interventions have been done (mostly in the United States, Canada, Europe, and Australia) and where there is more potential for population-level effects that will affect global health (low-and middle-income countries).More recently, Ramírez Varela et al 2 found a more than 50-fold difference in publications per 100,000 inhabitants comparing high-and lowincome countries.JPAH is not only committed to closing this gap, but also to make sure neither science nor scientific publishing has borders. 3In 2022, 41.2% of the submissions JPAH received came from the Global South.We expanded our free access policy, so that more articles were made available to nonsubscribers, particularly those coming from the Global South, for a limited time.Another piece of good news relates to another rise in JPAH's Impact Factor, from 2.59 to 3.00.This continued increase in our Impact Factor is a result of the excellent leadership of Dr. Loretta DiPietro, our former Editor-in-Chief, and also reflects the quality of the science we publish.For example, an article published in 2021 on the synergies between physical activity and the United Nations Sustainable Development Goals has been cited 67 times in such a short time frame. 4 From January 1 to December 31, 2022, JPAH received 676 article submissions, compared with 819 submissions in 2021.This 18% reduction in the number of submissions could be interpreted as bad news, but it had no impact on the proportion of desk rejections (70.9% in 2021 and 70.1% in 2022).In fact, out of the articles that made it to the peer-review stage, 56% ended up being accepted, compared with 52% in 2021.The combination of a slight reduction in submissions and a slight increase in acceptance rates arguably means that the awareness of JPAH's publishing priorities has increased among authors.Securing peer reviewers is JPAH's main challenge nowadays.The average time from submission to first decision, for the articles that made it to peer review, increased in 2022 to 55 days.The main reason is that our Senior Associate Editors are
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 | 0.019 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.044 | 0.019 |
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".