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Record W3008524785 · doi:10.1177/0095327x20905121

Experiences of Officer Cadets in Canadian Military Colleges and Civilian Universities: A Gender Perspective

2020· article· en· W3008524785 on OpenAlexaffabout
Grazia Scoppio, Nancy Otis, Yan Yan, Sawyer Hogenkamp

Bibliographic record

VenueArmed Forces & Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsQueen's UniversityDepartment of National DefenceRoyal Military College of Canada
Fundersnot available
KeywordsOfficerPerspective (graphical)PsychologyCadetMilitary personnelMilitary psychologyPolitical scienceManagementMedical educationMedicineLaw

Abstract

fetched live from OpenAlex

This study examined gender differences in the experiences of 923 officer cadets attending Canadian Military Colleges and 135 officer cadets attending civilian universities who completed a survey. Overall, the findings revealed that the experience of officer cadets in civilian universities was more positive, gender neutral, and their institutions’ values and culture were a better fit for them compared to their peers in Canadian Military Colleges. For officer cadets in Canadian Military Colleges, the results revealed that women were less likely to perceive gender equality in the way they were treated, the fairness of complaint mechanisms, and being treated with respect compared to men. Men in Canadian Military Collegess were less likely to perceive gender equality in performance standards than women. There were no gender differences in experiences for officer cadets in civilian universities.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0170.005
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.050
GPT teacher head0.276
Teacher spread0.227 · 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 designQualitative
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
Published2020
Admission routes2
Has abstractyes

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