Bibliographic record
Abstract
A few issues back, I wrote a slightly tongue in cheek article (Norman 2014) that identified the serious problem in the field that, while many articles are submitted to journals in health sciences education, few are accepted.And of those rejected by a first journal, relatively few are ever published.The bulk of the editorial was devoted to describing the various ways that authors ensure that their paper will not be published.As we examine the trends in publication in health science education, some paradoxical issues emerge.On the one hand, it seems that every journal's Impact Factor is inching upwards.So more and more articles are being cited.Similarly, the number of journals, both open access and mainstream is constantly growing.But acceptance rates are gradually falling.Thus, while there are more downloads, more citations, etc. in fact the number of submissions is growing at a far faster rate than the number of acceptances.For AHSE, this is dramatically illustrated in the two accompanying figures.We can see a dramatic increase in the total number of submissions.This has tripled in the 5 years from 2008-2009, from about 200/year to more than 600/year this year.In fact, after a period of relative stability, submissions have increased by 30 % this year, catching us all by surprise.However the acceptance rate has not kept pace, and has actually fallen from about 25 % in 2008-2009 to about 12-14 % now (Figs. 1, 2).Both of these factors-increased submissions but reduced acceptances-have increased pressure on the journals in a number of ways.And this created a ''perfect storm'' at AHSE that we were only dimly aware of in January, but we soon felt the impact over the past few months of this year.First of all, increased submissions.The most obvious effect of this was that the review process became overloaded.We must confess that, to our intense disappointment, the average time to review a manuscript has increased from 83 days last year to 111 days this year, for those manuscripts that are sent out for review.Similar proportional increases have
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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 | 0.009 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.093 | 0.072 |
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".