Promising Practices for Increasing the Number of Equity-Owed Lifeguards in Canada and the United States of America
Bibliographic record
Abstract
Currently a shortage of lifeguards exists in Canada and the United States (US) (Frommer, 2023; Santucci, 2024), and those who are working as lifeguards often do not reflect the diversity of the communities they serve (Martinez, 2020). Increasing the number of lifeguards from equity-owed groups is a potential way of increasing the lifeguard workforce and decreasing the shortage of workers in these important roles, as well as a moral imperative. In this paper I explore challenges to and promising practices aimed at increasing the representation of members equity-owed groups in lifeguarding. I begin with an overview of lifeguarding in Canada and the US. Then, we summarize key challenges to the inclusion of members equity-owed groups within lifeguarding. Next, I use archival research, an intersectional theoretical approach, and reflexive thematic analysis to highlight promising practices for promoting equitable access and inclusion for members of equity-owed groups in lifeguarding.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".