Coaches’ and Athletic Directors’ Use of Strengths in Implementing Policy: An Exploratory Study of Transgender Policies in U SPORTS and CCAA from a Strengths and Hope Perspective
Bibliographic record
Abstract
In September 2018 U SPORTS released for the first time a transgender policy; CCAA had released their policy seven seasons earlier. Currently there exists no research on how sport administrators (i.e., coaches and athletic directors) might implement these policies, which leads to the purpose of this exploratory study, which was to examine how coaches and athletic directors (ADs) might implement transgender related policy in U SPORTS and CCAA. Framed within a strengths and hope perspective (Paraschak, 2013b), participants’ shared preferred futures were established (Jacobs, 2005) as well as an understanding of how they shaped and simultaneously were shaped by others. A multi-method approach was used for this study. Nine semi-structed interviews were completed: three ADs and six coaches. Interviews were coded using open and focused coding (Van Den Hoonaard, 2012). Further, U SPORTS 80.80.5 Transgender Student-Athlete and CCAA Operating Code Article 5 – Eligibility Section 16 Policy on Transgender Student-Athletes were examined using discourse analysis, which looks at how documents can be recontextualized (Spratt, 2017). Three forms of success emerged: athletic, academic and intra/interpersonal well-being; however, only intra/interpersonal well-being was linked to policy implementation by the interviewees. Strengths that emerged were communication, openness, inclusion and prior experiences. Further, participants identified the following resources to further their ability to achieve a preferred future: material and especially human resources. Finally, participants believed they could be a resource for others by using their communication skills with an openness and willingness to discuss prior experiences tied to the policy.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".