COVID-19 impacts on sport governance and management: a global, critical realist perspective
Bibliographic record
Abstract
This commentary considers the impacts of COVID-19 on sport governance and management, given the global threat to sport services and organizations evident as a result of the disease since early 2020. To frame this analysis of the impacts and lessons to be learned, we use a Critical Realist (CR) perspective, which takes a multi-level view of reality and seeks to establish how and why something occurs in reality [Byers, T. (2013). Using critical realism: A new perspective on control of volunteers in sport clubs. European Sport Management Quarterly, 13(1), 5–31. https://doi.org/10.1080/16184742.2012.744765]. While the existing commentaries and emerging research on COVID19 have focused on a superficial level of reality (i.e. what stakeholder responses have been), a CR view encourages a more holistic account of what and why something happens. Specifically, this commentary contributes to the discussion of COVID-19 impacts focusing on sport governance, using a philosophy that encourages examination of what is happening in sport organizations, how different stakeholder’s perspectives and assessment of the legitimacy of COVID-19 may reveal underlying social structures and biases that help explain sport administrator’s responses and value systems. We hope this novel perspective on sport governance encourages readers to think of new ways of organizing and governing that is more inclusive of diversity (e.g. race, gender, disability) in sport.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".