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Record W7110028171 · doi:10.26522/jess.v11i.5012

Perceptions of Institutional Resource Inequities at FCS and Low-Major NCAA Programs

2025· article· W7110028171 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Emerging Sport Studies · 2025
Typearticle
Language
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsEquity (law)PerceptionResource (disambiguation)Grounded theoryFocus groupInequalitySubdivisionChampionship

Abstract

fetched live from OpenAlex

This study delves into the perceptions of student-athletes from Football Championship Subdivision (FCS) and low-major NCAA Division I programs, regarding the disparities in institutional resources. Employing a grounded theory approach, the research compiles data from interviews, questionnaires, and focus groups involving 81 student-athletes across four different universities. It highlights the perceived inequities in resources, such as facilities, equipment, and support staff, alongside the effects of these disparities on athletes’ health, recruitment, mental wellness, and academic achievement. The findings suggest a profound impact of resource inequities on the competitive and personal development of student-athletes. This research contributes to the ongoing dialogue on equity in collegiate athletics, providing insights that could guide policy and resource allocation decisions to promote a more equitable environment for student-athletes across various divisions. It also introduces the development of a new theory referred to as institutional athletic privilege.

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)0.007
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 score0.068
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.039
GPT teacher head0.346
Teacher spread0.308 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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