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Record W2335762647 · doi:10.1177/0894845316635821

Intentions to Be an Athletic Director

2016· article· en· W2335762647 on OpenAlexaff
Janelle E. Wells, Shannon Kerwin

Bibliographic record

VenueJournal of Career Development · 2016
Typearticle
Languageen
FieldHealth Professions
TopicAthletic Training and Education
Canadian institutionsBrock University
Fundersnot available
KeywordsPsychologyMultivariate analysis of varianceSocial cognitive theorySocial psychologyAthletesMedical educationMedicine

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate senior athletic administrators’ expectations and intentions of becoming National Collegiate Athletic Association (NCAA) Division I athletic directors (ADs) and explore women and racial minority senior athletic administrators’ athletic workplace experience. To serve the purpose, two studies using social cognitive career theory (SCCT) were employed. First, demographic (i.e., gender and race) differences by SCCT variables were assessed through survey collection and multivariate analysis of variance. Second, content analysis of interviews was used to assess the experiences of athletic administrators. Results revealed women and racial minority senior athletic administrators’ had similar self-efficacy compared to White men, but they encountered more barriers, unfavorable outcome expectations, and lower choice goals associated with becoming an NCAA Division I AD. Further, findings show women and racial minority senior athletic administrators felt occupational segregation limited their access and opportunities for career advancement to a Division I AD position.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.190
GPT teacher head0.428
Teacher spread0.238 · 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 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

Citations17
Published2016
Admission routes1
Has abstractyes

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