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Record W33511643 · doi:10.1111/cdev.13525

Women as directors of NCAA Division I intercollegiate athletics: Defining experiences and leadership styles

2012· article· en· W33511643 on OpenAlexfundno aff
Constance Therese Skinner

Bibliographic record

VenueChild Development · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaHorizon 2020 Framework ProgrammeConcordia UniversityAcademy of Finland
KeywordsDivision (mathematics)College athleticsManagementPublic relationsPolitical sciencePsychologyLawEconomicsHigher education

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the personal and professional experiences perceived by NCAA Division I female intercollegiate directors of athletics. This qualitative piece sought to gain knowledge about female directors of athletics within their workplace environments and their leadership lifestyles. Data was collected through interviewing the participants (N=9) over the telephone. The interviews were semi-structured and open-ended to follow a guided interview guide approach. Using social constructivism and narrative analysis along with a hybrid thematic analysis approach of both inductive and deductive interpretation, data analysis revealed four hierarchical order themes with subcategorical first- and second-order themes. The four major themes that emerged from the data were personal attitudes towards leadership, experiences within current leadership position, thoughts on being a female director of athletics, and contributors to leadership success.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.064
GPT teacher head0.287
Teacher spread0.223 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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