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Record W2802775023 · doi:10.3138/jvme.1116-176r1

The Challenge of “Fixing the Debt”: Recommendations from the Summit

2018· article· en· W2802775023 on OpenAlexvenueno aff
Donna K. Harris, Harry M. Chaddock

Bibliographic record

VenueJournal of Veterinary Medical Education · 2018
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSalarySummitDebtStudent debtState (computer science)MedicineMedical educationPolitical scienceVeterinary medicineBusinessFinanceLawGeography

Abstract

fetched live from OpenAlex

With education debt repayment taking up a significant amount of veterinarians' salaries, for a significant time into the working years, concern has been building that the current debt to starting salary ratio in the veterinary profession is not sustainable. The current ratio is 1.99:1, but it can be significantly higher for students who attend schools as an out-of-state resident. In April, 180 people concerned about this issue gathered at Michigan State University's College of Veterinary Medicine for a Fix the Debt Summit, which focused on actions that would reduce this ratio to a more sustainable level. Attendees were students; new graduates; those working in veterinary academia; employers of veterinarians; and those affiliated with the profession, such as professional associations. As solutions were proposed, participants also committed to taking action within their field of influence.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient 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 score0.464
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.423
GPT teacher head0.557
Teacher spread0.134 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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