Design archetype utility for understanding and analyzing the governance of contemporary national sport organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
More than 30 years has passed since the seminal works of the late Trevor Slack and his colleagues first appeared using design archetypes to explore change dynamics amongst National Sport Organizations. The scale and nature of change that has continued to occur in the operating environment for these organizations over the last three decades has resulted in NSOs increasingly being required to be more professional, to manage more complex sport delivery systems, and strategically adapt and change to be effective. In that context, we explore the contemporary nature of NSO design archetypes in order to better understand the current and emerging dynamics of change for these organizations. This conceptual paper reviews the theory and utility of design archetypes in helping to understand organizational change dynamics in relation to NSOs, revisits the work of Slack and others in order to identify ways to improve the representation of design archetypes for contemporary NSOs, and presents an argument for a renewed emphasis on design archetypes as a fundamental driver for future research efforts to help understand change within NSOs, and indeed, other sport organizations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it