Assets and obstacles: an analysis of OUA hockey from the coaches’ perspective
Bibliographic record
Abstract
Purpose The passion of Canadians for ice hockey is well documented; however, university teams in Canada are routinely ignored by consumers and the media. The authors’ goal was to better understand the context in which Ontario university hockey struggles and to address the theoretical question of how best to examine and evaluate the problems of sport‐specific organizations. Using the Value Dynamics Framework (VDF), the purpose of this paper was to examine whether or not this framework fits well with the realities facing not‐for‐profit OUA hockey teams, and if not, to create a framework specific to these teams. Design/methodology/approach Semi‐structured in‐depth interviews were conducted with 15 of the 19 (77 percent) OUA hockey coaches during the 2010/2011 hockey season. The interview guide was drawn from the VDF elements and enabled the researchers to understand not‐for‐profit organizational assets, including physical, financial, employee/supplier, customer, and organizational. Findings This paper offers empirical insights about the assets and obstacles facing the OUA hockey league and its teams. For example, players, coaches, affiliation with universities, and the hockey product are noted assets. Obstacles for strategic growth include arenas, suppliers, media attention, financial sustainability, parity with other leagues in Canada, and leadership. The VDF proved a useful foil to suggest that something is needed that more accurately represents sport management‐specific situations. Research limitations/implications The main limitation of this study is that it lacks generalizability. Although motivated to better understand not‐for‐profit sport in general, the authors’ model is specific to OUA men's hockey teams. However, their OUA hockey team‐specific revised VDF does provide insights into the assets available to coaches, and also acknowledges the corresponding challenges or obstacles surrounding the asset classes in the context of OUA hockey. Practical implications This paper provides an approach towards making a more generalizable not‐for‐profit sport model that could help explain the success (or lack of success) of such organizations. Originality/value This study addresses a need to develop a framework to examine and evaluate not‐for‐profit sport‐specific organizations, such as the teams in the OUA.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".