National sport organization governance design archetypes for the twenty-first century
Bibliographic record
Abstract
Research question: This paper revisits our knowledge of sport organization governance design archetypes. To do so, we focus on Canadian national sport organizations (NSOs) and pose three research questions: (1) what governance design archetypes exist based on the use of more contemporary criteria; (2) how easily can an NSO’s archetype be determined; and (3) what are the implications of these new archetypes for researchers and practitioners? Research methods: We undertook a landscape study of 32 Canadian NSOs with data from an online survey, publicly-available information, and clarification calls. Archetypes were derived from 47 organizational and governance characteristics using a k-means cluster analysis. Results and Findings: Our empirically-derived archetype design taxonomy showed the best fit to be four clusters (Board-led, Executive-led, Professional, and Corporate) based on key organizational values, complexity, capacity, revenue sources, and governance variables. Implications: Besides knowing NSOs are more heterogenous than in the past, researchers and practitioners can use capacity, efficiency, horizontal differentiation, broadcast revenue, political accountability, and social media information to derive an NSO’s governance archetype. These findings imply researchers can (1) examine non-profit sport organizations’ changes over time based on a set of archetypes reflecting contemporary realities, and (2) compare and contrast NSOs’ governance more holistically. In turn, managers can better compare their NSO with other NSOs to optimize their organization’s performance. Finally, national sport agencies/funders should support NSOs’ governance improvement efforts through flexible guidelines and resources because of NSOs’ governance heterogeneity.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".