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Record W2045568914 · doi:10.3138/jvme.29.3.162

An Exercise in Leadership Training for Veterinary Students Aiming for Careers In Biomedical Research

2002· article· en· W2045568914 on OpenAlexvenueno aff
David R. Fraser, Douglas D. McGregor

Bibliographic record

VenueJournal of Veterinary Medical Education · 2002
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
FundersRichard King Mellon Foundation
KeywordsModerationSession (web analytics)Theme (computing)Medical educationPsychologyLeadership studiesLeadership styleMedicineComputer science

Abstract

fetched live from OpenAlex

A group discussion on the theme of "leadership" has been a central event in the annual Cornell Leadership Program for Veterinary Students since 1990. However, these discussions were often unfocused and did not readily demonstrate the leadership skills of distinguished guests who were invited to participate. Since 1998, a new format for this session has been developed in which students and guests are assigned individual roles in a scenario that is unfolded by a moderator over two to three hours. This role-playing exercise ensures that every student is obliged to participate and has an opportunity to practice such leadership skills as critical thinking, verbal communication, and decision making under pressure and with inadequate information. The distinguished guests, in their assigned roles, are able to interact freely with the student fellows and thus demonstrate their expertise as experienced leaders. This challenging experience has become an enjoyable part of the 10-week Leadership Program and one that shows the importance of leadership skills for those who aspire to careers in the biomedical sciences.

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)0.006
metaresearch head score (Gemma)0.010
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0070.003
Scholarly communication0.0040.003
Open science0.0020.011
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0190.014

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 head0.879
GPT teacher head0.669
Teacher spread0.211 · 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
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

Citations8
Published2002
Admission routes1
Has abstractyes

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