Understanding the Ecological System: Increasing Women’s Sport Participation Within Bowls Canada Boulingrin
Bibliographic record
Abstract
This case showcases Amber, the CEO of Bowls Canada Boulingrin (Bowls Canada). Amber and her Board of Directors are looking for ways to increase the number of adult women1 who participate in bowls at the competitive level. Through the case narrative and teaching note, students are asked to explore Bowls Canada’s strategic plan to uncover the organization’s goals around equity, diversity, and inclusion, and review data that has been collected related to barriers for women’s participation in competitive Bowls. Analysis of the data is linked to Social Ecological Theory, which will help students think about the most effective ways to synthesize their understanding of barriers into recommendations for Amber and her Board. The need to use data to make decisions around issues related to equity, diversity, and inclusion is increasingly relevant when addressing multiple concepts in the sport management classroom. The case contributes to engaging discussion that will naturally generate valuable interaction among students.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.042 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".