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Record W4395453377 · doi:10.1080/14927713.2024.2335227

Les effets ambivalents de la culture sportive dans une opération de fusion-acquisition : le cas du groupe Boardriders et des marques iconiques dans l’industrie des action sports

2024· article· fr· W4395453377 on OpenAlexvenueno aff
Pierre Durand

Bibliographic record

VenueLeisure/Loisir · 2024
Typearticle
Languagefr
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Les organisations sportives marchandes occupent une place croissante dans le milieu sportif, notamment celles qui évoluent dans l’industrie des articles de sports. Ces dernières sont confrontées à des spécificités exogènes qui interagissent avec leur stratégie comme le révèle le cas étudié ici du groupe Boardriders. Analysés au prisme de la théorie de la contingence et des apports de la sociologie du sport, les résultats tirés d’une quinzaine d’entretiens semi-directifs menés auprès des professionnels de terrain (opérateur, cadre intermédiaire, licencié) et d’une exploitation des archives de presse mettent en lumière la stratégie de fusion-acquisition initiée par le fonds d’investissement Oaktree Capital Management à partir des entreprises Quiksilver et Billabong, ainsi que de leur portefeuille de marques. Ces résultats révèlent que les entreprises et la culture sportive forment un point d’appui opportun pour réaliser une fusion emblématique sur le marché.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.008
Scholarly communication0.0060.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.030
GPT teacher head0.342
Teacher spread0.312 · 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
Published2024
Admission routes1
Has abstractyes

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