MétaCan
Menu
Back to cohort
Record W2889815231 · doi:10.1080/23750472.2018.1519376

The governing of governance: metagovernance and the creation of new organizational forms within Canadian sport

2017· article· en· W2889815231 on OpenAlexaffabout
Mathew Dowling, Marvin Washington

Bibliographic record

VenueManaging Sport and Leisure · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNegotiationCorporate governanceGovernment (linguistics)Public relationsDiplomacyBusinessControl (management)Political scienceKnowledge managementManagementPoliticsEconomics

Abstract

fetched live from OpenAlex

This article examines how the creation of new governing arrangements (i.e., new organizational forms and new actors) has enabled government (Sport Canada) to strengthen its control and influence over Canadian sport illustrated through two empirical case studies of newly created organizational forms. More specifically, we draw upon the metagovernance approach to examine the inter-organizational dynamics between Sport Canada and quasi-autonomous organizational entities (Own the Podium and Sport for Life) and the nature of these new governance arrangements. The analysis highlights the similar trajectories of the two case-study organizations and reveals similar underlying patterns of control by Sport Canada in that both newly created entities have been used to strengthen the governmental agencies’ capacity and reach over the sport sector. Furthermore, the study reveals how these resource-dependent organizations are being utilized and leveraged by government, often through negotiation, diplomacy, and other informal mechanisms, to achieve its own objectives. More broadly, our analysis highlights the underlying mechanisms through which these newly formed networked arrangements operate.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.850

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueManaging Sport and LeisureSame topicSport and Mega-Event ImpactsFrench-language works237,207