Saving the Life of a National Sport Organization With Strategy and Governance
Bibliographic record
Abstract
Endurance Canada is facing a dire situation. It must turn itself around or face bankruptcy. The problems are piling up for Endurance Canada’s Board of Directors. Over the last 20 years, the Board’s Chair, Bill Fitzpatrick, has seen his sport’s athletes go from winning Olympic medals to not having any medals internationally. Within Endurance Canada, he has seen high staff turnover and burnout, power struggles between the national and provincial/territorial levels, and their revenues have been hit hard. COVID-19 was the proverbial nail in the coffin. Something drastic needs to happen. So, Bill brings in Amanda Tsang, a strategy expert. She has 1 month to come up with a plan to bring Endurance Canada back to life. The case follows Amanda as she reviews the strategy, structure, and governance issues in the organization. This fictional case asks students to (a) develop a strategic plan for Endurance Canada, (b) show how structure and strategy are interrelated, and (c) reflect on governance issues in a multilevel governance system.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.017 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".