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Record W2472105250 · doi:10.1017/cem.2015.63

CAEP 2014 Academic Symposium: “How to make research succeed in your emergency department: How to develop and train career researchers in emergency medicine”

2015· editorial· en· W2472105250 on OpenAlexafffund
Jeffrey J. Perry, J.D. Artz, Ian G. Stiell, Sedigheh Shaeri, Shelley McLeod, Natalie Le Sage, Corinne M. Hohl, Lisa A. Calder, Christian Vaillancourt, Brian R. Holroyd, Judd E. Hollander, Laurie J. Morrison

Bibliographic record

VenueCanadian Journal of Emergency Medicine · 2015
Typeeditorial
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsUniversity of ManitobaUniversity of AlbertaUniversité LavalCanadian Association of Emergency PhysiciansUniversity of TorontoUniversity of OttawaVancouver Coastal HealthSt. Michael's HospitalWestern University
FundersOttawa Hospital Research InstituteDalhousie UniversityChina Academy of Engineering PhysicsSociety for Academic Emergency Medicine
KeywordsMentorshipSalaryMedicineMedical educationEmergency departmentMEDLINEFamily medicineNursingPolitical science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.034
metaresearch head score (Gemma)0.108
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.984
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.108
Meta-epidemiology (narrow)0.0060.003
Meta-epidemiology (broad)0.0070.007
Bibliometrics0.0090.004
Science and technology studies0.0120.006
Scholarly communication0.0200.010
Open science0.0090.006
Research integrity0.0650.060
Insufficient payload (model declined to judge)0.0150.012

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 head0.456
GPT teacher head0.517
Teacher spread0.062 · 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
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

Citations26
Published2015
Admission routes2
Has abstractno

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