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Record W4200298151 · doi:10.3138/jmvfh-2021-0080

A gender-based analysis of recruitment and retention in the Canadian Army Reserve

2021· article· en· W4200298151 on OpenAlexaffvenueabout
Stéfanie von Hlatky, Bibi Imre‐Millei

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsQueen's University
Fundersnot available
KeywordsMentorshipGender equityPsychologyMisconductPublic relationsMedical educationSocial psychologyPolitical scienceApplied psychologyMedicineSociologyGender studiesLaw

Abstract

fetched live from OpenAlex

LAY SUMMARY In this qualitative study, 29 members of the Canadian Army Reserve were interviewed to investigate Canadian Armed Forces (CAF) recruitment and retention strategies. Studying member attitudes and participation in recruitment and retention led to original insights about the importance of community outreach, peer recruiting, and commitment on behalf of leadership when it comes to fostering a recruitment-focused culture. Participants pointed to camaraderie and the quality of training opportunities as significant considerations to improve retention, providing further validation to existing research on retention in reserve units. Using a gender-based lens, reservists were asked about the culture of the CAF, sexual misconduct, and issues facing under-represented groups. Participants felt the military was doing well meeting recruiting targets and that representation and mentorship were important tools to encourage women and members of under-represented groups to join. The answers regarding sexual misconduct were extremely consistent: most were surprised when hearing Reserve Force statistics on sexual misconduct, and many displayed low awareness of how to report incidents. Nevertheless, participants thought their units were better than others when it came to equity, diversity, inclusion, and preventing sexual misconduct, signalling these topics could be further examined in the reserves.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.214
GPT teacher head0.382
Teacher spread0.168 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2021
Admission routes3
Has abstractyes

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