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Record W2916965736 · doi:10.15694/mep.2019.000036.1

Curricular Monikers: What's in a Name?

2019· article· en· W2916965736 on OpenAlexaboutno aff
Arnyce R. Pock, Steven J. Durning, William R. Gilliland, Martin G. Ottolini, Louis N. Pangaro

Bibliographic record

VenueMedEdPublish · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

<ns4:p>This article was migrated. The article was not marked as recommended. Introduction: An increasing number of North American medical schools are assigning unique names ("monikers") to their undergraduate curricula, but it is unclear as to how often this occurs, and what kind of names schools are choosing. Method: A manual review of the 160 websites that corresponded to Schools of Medicine that were either fully or provisionally accredited by the Liaison Committee on Medical Education (LCME). Results: 31.5% of the 143 U.S. allopathic medical schools and only one (5.8%) of the 17 LCME accredited Canadian medical schools currently associate a unique curricular name with their undergraduate medical education programs. Use of a constant-comparative technique suggested that schools that did assign a curricular name to their programs had selected names that aligned to one of eight over-arching themes. Conclusions: While curricular names were somewhat less commonly applied in schools located in the western United States, no specific trends in thematic choices predominated in any geographic region. However, the impact of curricular themes on current and/or prospective medical students remains an area for continued exploration, as does the longitudinal question as to whether thematically named curricula succeed in delivering their intended results.</ns4:p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.297
Teacher spread0.286 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
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

Citations0
Published2019
Admission routes1
Has abstractyes

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