Bibliographic record
Abstract
Chapter outlineAs with many other domains of sport, the lack of women's involvement within the formalized setting of governance has raised concern from scholars and policy makers alike (Adriaanse, 2016;Henry & Robinson, 2010).Despite calls for greater gender diversity within sport governance, women are still strongly underrepresented across international, national, and local levels (Burton, 2015; Sibson, 2010).The purpose of this chapter is to explore the topic of women's involvement in sport governance, and in doing so help explain why women are still largely absent from sport governance, highlight the contribution women can make to sport governance, and to suggest ways forward for research and practice.We do this by first providing some background about sport governance scholarship, which leads us to identify predominant theories that have been used to explain sport governance and gender dynamics.We particularly highlight institutional theory as a lens to examine women's involvement in sport governance as well as considering the small number of studies that have focused on gender and sport governance.Next, we offer a contextualized account of women's involvement in sport governance by using New Zealand Rugby (NZR) to explore the forces that have influenced the governance of that sport.Institutional theory is used here to help explain this setting.We conclude by highlighting a landmark in the governance journey of NZR, the inclusion of the first women to the national board in 2016.Farah Palmer's backstory is offered as our leader profile before we conclude and offer future directions for research and practice. Background to the study of sport governanceThe field of sport governance scholarship has gained moment over the past 15 years where organizational level governance has dominated the literature (O'Boyle & Shilbury, 2016).Sometimes referred to as 'corporate governance', organizational governance focuses on the board grouping and/or individuals charged with the responsibility of governing sport organizations (Henry & Lee, 2004).Commonwealth countries such as Australia, New Zealand, Canada, and the United Kingdom, as well as Greece, Portugal, Spain, and Taiwan, where the sports systems are dominated by non-profit organizations have been the primary contexts for investigation (Shilbury, Ferkins & Smythe, 2013).Within these countries, scholars have found common topics and issues that have helped to strengthen our understanding of sport governance as a global concern (Hoye & Cuskelly, 2007;Hoye & Doherty, 2011).Early work established
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".