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Record W3084822711 · doi:10.1123/tsp.2019-0087

The Birth of the Stars: A Participatory and Appreciative Action and Reflection Investigation into the Leadership and Development of a New Superleague Netball Club

2020· article· en· W3084822711 on OpenAlexaff
Anita Navin, Don Vinson, Alison Croad, Jennifer Turnnidge, Jean Côté

Bibliographic record

VenueThe Sport Psychologist · 2020
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsQueen's University
Fundersnot available
KeywordsClubPsychologyTransformational leadershipAppreciative inquiryAction (physics)Social psychologyPublic relationsSociologyPedagogyPolitical science

Abstract

fetched live from OpenAlex

This Participatory and Appreciative Action and Reflection (PAAR) investigation illustrates a leader’s first steps in a “values-to-action” journey. Drawing on the interface between transformational leadership and organizational culture, this study focused on the birth of the Severn Stars—a professional netball club in the United Kingdom. In particular, this PAAR investigation explored how the leader’s values were operationalized through the club’s inaugural year. Fourteen operational managers, coaches, and players were individually interviewed in order to gain an appreciative gaze and subsequently reframe their lived experience. Results demonstrated how transformational leadership was manifested through the pragmatic deployment of club values and how the organizational culture was, in part, characterized by individualized consideration, intellectual stimulation, idealized influence, and inspirational motivation. These behaviors and the organizational culture were shown to enhance prosocial relationships and social connections across the club, the influence of the Super Stars, and stakeholders’ perceptions of autonomy.

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)0.007
metaresearch head score (Gemma)0.011
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0110.010
Scholarly communication0.0070.003
Open science0.0010.008
Research integrity0.0010.004
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 head0.291
GPT teacher head0.384
Teacher spread0.093 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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