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Record W3192228297 · doi:10.3390/vetsci8080159

Women Representation and Gender Equality in Different Academic Levels in Veterinary Science

2021· article· en· W3192228297 on OpenAlexaboutno aff
Xinyue Liu, Rebecca A. Dunlop, Rachel Allavena, Chiara Palmieri

Bibliographic record

VenueVeterinary Sciences · 2021
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStaffingAccreditationGender disparityInequalityGender equalityDistribution (mathematics)Gender biasVeterinary medicinePolitical scienceMedical educationMedicineDemographySociologyPsychologyGender studiesNursing

Abstract

fetched live from OpenAlex

Women's participation and completion at veterinary schools has increased globally for the past few decades. However, increased female graduates have not translated into similar patterns of academic staffing. The gender distribution within each academic level at eight accredited veterinary faculties in Australia and New Zealand, 38 accredited faculties in the USA and Canada and 98 accredited faculties in Europe were analyzed. Women occupied 47.9%, 45.5% and 47.5% of the academic positions in Australia/New Zealand, the USA/Canada and Europe, respectively. Compared to their male counterparts, female academics were more likely to hold the lower ranked positions. The gender distribution is skewed toward men in the senior positions at or above associate professor level in all analyzed regions. The findings of this study confirm gender inequality in academic progression meaning there is a continued need to develop strategies to eliminate inequity in veterinary science faculties worldwide.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.765
GPT teacher head0.607
Teacher spread0.158 · 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 designObservational
DomainIncentives
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

Citations16
Published2021
Admission routes1
Has abstractyes

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