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Record W3190784172 · doi:10.1007/s10459-014-9539-z

Taking stock

2014· editorial· en· W3190784172 on OpenAlexaff
Geoff Norman

Bibliographic record

VenueAdvances in Health Sciences Education · 2014
Typeeditorial
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

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bulk of the editorial was devoted to describing the various ways that authors ensure that their paper will not be published.As we examine the trends in publication in health science education, some paradoxical issues emerge.On the one hand, it seems that every journal's Impact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

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.093
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.061
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0110.006
Open science0.0020.002
Research integrity0.0090.014
Insufficient payload (model declined to judge)0.0930.072

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.

Opus teacher head0.018
GPT teacher head0.458
Teacher spread0.439 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations2
Published2014
Admission routes1
Has abstractyes

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