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Record W2344955939 · doi:10.1123/jsm.2015-0368

An Analysis of Countries’ Organizational Resources, Capacities, and Resource Configurations in Athletics

2016· article· en· W2344955939 on OpenAlexaboutno aff
Jasper Truyens, Veerle De Bosscher, Popi Sotiriadou

Bibliographic record

VenueJournal of Sport Management · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsEliteOrganizational performanceBusinessOrganizational behavior and human resourcesPrioritizationOrder (exchange)Resource (disambiguation)Resource management (computing)Knowledge managementOrganizational commitmentProcess managementPublic relationsMarketingPolitical scienceComputer sciencePolitics

Abstract

fetched live from OpenAlex

Research on elite sport policy tends to focus on the policy factors that can influence success. Even though policies drive the management of organizational resources, the organizational capacity of countries in specific sports to allocate resources remains unclear. This paper identifies and evaluates the organizational capacity of five sport systems in athletics (Belgium [separated into Flanders and Wallonia], Canada, Finland, and the Netherlands). Organizational capacity was evaluated using the organizational resources and first-order capabilities framework (Truyens, De Bosscher, Heyndels, & Westerbeek, 2014). Composite indicators and a configuration analysis were used to collect and analyze data from a questionnaire and documents. The participating sport systems demonstrate diverse resource configurations, especially in relation to program centralization, athlete development, and funding prioritization. The findings have implications for high performance managers’ and policy makers’ approach to strategic management and planning for organizational resources in elite sport.

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 score0.565
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.268
Teacher spread0.258 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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