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Record W4416790819 · doi:10.1080/19406940.2025.2592563

What is the ‘i’ in team?: exploring individual role identity in sport board governance

2025· article· en· W4416790819 on OpenAlexaff
Talia Ritondo, Shannon Kerwin, Dawn E. Trussell, Teresa Hill, Erin Corkery

Bibliographic record

VenueInternational Journal of Sport Policy and Politics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsBrock University
Fundersnot available
KeywordsCorporate governanceIdentity (music)Perspective (graphical)Government (linguistics)Governmentality

Abstract

fetched live from OpenAlex

Our study explored regional volunteer sport board member role identities relative to performance regimes and power relations. Specifically, we explored the intersection between board members’ role identities (conscious and unconscious) and power relations and how they shaped the adoption or resistance of performance regimes. Guided by a critical ethnographic perspective, data were collected by observing 58 board meetings and 30 semi-structured interviews. Key findings illustrated how board members with similar role identities exercised the most power on their board and directed how they subscribed to performance regimes, creating in-groups and out-groups on boards based on conscious and unconscious role identities. Further, board members’ relationships with others within the provincial governing body (i.e. volunteer or employee) were influenced by their role identities and, in turn, their compliance with or resistance to performance regimes. Finally, role identities and power relations defined board members’ criteria for success in performance regimes, which occurred both in board in-groups and individually. This study makes a theoretical contribution to sport studies literature by applying role identities theory to understand who exercises power on volunteer sport boards and how the adoption or resistance of performance regimes was shaped by identities and power relations in sport board governance.

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)0.001
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 score0.573
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.038
GPT teacher head0.359
Teacher spread0.322 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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