Challenges of the Canadian Premier League in its first season : learnings from early experience and contrasting with business start-ups
Bibliographic record
Abstract
In 2019, the Canadian Premier League (CPL) kicked off its inaugural season. The CPL is a newly established tier-one domestic entity that filled a void for professional football in Canada. Previous attempts to establish a professional football league in the country failed due to various reasons. League officials seemed to face insurmountable challenges prior to the start of the league’s inaugural season. Nevertheless, the new venture concluded its first season and received great reviews from local media. Little research has been published regarding the CPL in general and on its challenges in particular. Therefore, this thesis investigates the challenges of the Canadian Premier League in its first season and additionally contrasts the findings with obstacles regular business start-ups are facing. \nThe researcher utilized a qualitative research approach for this study. Semi-structured interviews with open-ended questions were conducted with eight study participants. The research shows that league officials did a great job of anticipating potential challenges and put a sound league structure in place that facilitates the viability of the league and its clubs. However, the study also shows that the CPL and its clubs faced both, previously known and unknown obstacles that appeared prior, as well as during the inaugural season. Many of the league’s identified challenges are similar to the ones regular business start-ups are facing. However, both entities are difficult to contrast as they demonstrate stark differences. Sport in general is a particular business and the CPL sets itself apart due to its community involvement and unique ownership structure.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.032 | 0.016 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".