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Record W2753627744 · doi:10.3138/jvme.0217-029r

Curriculum Review and Revision at the University of Minnesota College of Veterinary Medicine

2017· article· en· W2753627744 on OpenAlexvenueno aff
Margaret V. Root Kustritz, Laura K. Molgaard, Erin Malone

Bibliographic record

VenueJournal of Veterinary Medical Education · 2017
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumMandateMedical educationCurriculum developmentPolitical scienceMedicineSociologyPedagogy

Abstract

fetched live from OpenAlex

Curriculum review is an essential part of ongoing curriculum development, and is a mandate of the American Veterinary Medical Association Council on Education (AVMA COE), the accrediting body of all North American schools and colleges of veterinary medicine. This article describes the steps in curriculum review undertaken by the University of Minnesota College of Veterinary Medicine (UMN CVM) in response to this mandate from the COE and to a recommendation from a recent collegiate review that was part of a larger university-level strategic planning effort. The challenges of reviewing and revising the curriculum within a short time frame were met by appointing a dedicated curriculum review board and by engaging students and faculty groups, both as focus groups and as specific faculty work sections within disciplines. Faculty voting on the process was very valuable as it permitted the curriculum review board and faculty groups to move ahead knowing there was a process in place for reassessment if most faculty did not agree with recommendations. Consistent support from the dean of the college and other administrators was vital in helping maintain momentum for curriculum review.

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)0.003
metaresearch head score (Gemma)0.004
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.302
GPT teacher head0.531
Teacher spread0.229 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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