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Record W3209343447 · doi:10.36834/cmej.72593

Energizing scholarly activity in a regional medical campus

2021· article· en· W3209343447 on OpenAlexaffvenueabout
Amanda Bell, Ekta Khemani, Seddiq Weera, Chris Henderson, Larry W. Chambers

Bibliographic record

VenueCanadian Medical Education Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcMaster UniversityRegional Municipality of Niagara
Fundersnot available
KeywordsAccreditationLibrary sciencePolitical scienceHumanitiesMedical educationMedicinePhilosophyComputer science

Abstract

fetched live from OpenAlex

Medical education is increasingly delivered at distributed sites away from Academic Health Sciences Centres. The Council of Ontario Faculties of Medicine recommends schools develop resources and metrics to foster regional campus scholarly activity. Opportunities for distributed program trainees must support learning core skills in research and critical appraisal to comply with medical school accreditation standards and to develop their interests and skills in scholarly work for their future medical careers. We describe a scholarly activity program that is a template for distributed campuses or regional teaching sites seeking to increase learner and faculty engagement and research productivity.

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)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0060.003
Scholarly communication0.0070.003
Open science0.0030.016
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0180.008

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 head0.333
Teacher spread0.315 · 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.

Study designNot applicable
DomainIncentives
GenreEmpirical

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

Citations6
Published2021
Admission routes3
Has abstractyes

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