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Record W2164556207 · doi:10.1123/jsm.2012-0304

Leader Perceptions of Management by Values Within Canadian National Sport Organizations

2014· article· en· W2164556207 on OpenAlexaffabout
Dina Bell-Laroche, Joanne C. MacLean, Lucie Thibault, Richard Wolfe

Bibliographic record

VenueJournal of Sport Management · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Strategy and Culture
Canadian institutionsUniversity of VictoriaBrock UniversityUniversity of the Fraser Valley
Fundersnot available
KeywordsPublic relationsPerceptionOrganizational cultureSport managementQualitative researchKnowledge managementBusinessSociologyPsychologyPolitical science

Abstract

fetched live from OpenAlex

This study examined sport leaders’ perceptions of the use of stated values in the management and performance of their organization. Qualitative data were collected from nine Canadian national sport organizations (NSOs) in a multiple-case studies design, involving analysis of interview transcripts. Results indicated that while many of the NSOs operated from a traditional management by objectives approach, they perceived management by values (MBV) as being important and contributing to enhanced organizational performance. Leaders indicated that more efforts to engage staff members in developing core organizational values and to strategically use values in day-to-day management practice were required. A 4-I Framework describing how an NSO can progress through different stages of strategically using values in management practice was developed. NSO leaders also voiced an interest in embedding organizational values into NSO strategic and other planning processes.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.578

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0090.005
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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

Citations22
Published2014
Admission routes2
Has abstractyes

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