Community sports fields and atmospheric climate impacts: Australian and Canadian perspectives
Bibliographic record
Abstract
© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. Purpose: This paper presents a study of atmospheric climate impacts on community-level sports clubs’ (CLSC) in Australia and Canada, their vulnerability and resilience, and organisational responses. Design/Methods: A qualitative methodology was used with a multiple case research design. Data (interviews, documents) was collected from a sample of 23 CLSC organisations managing grass turf sport fields exposed to climatic extremes in temperate regions of both countries. Findings: CLSCs in both nations experienced vulnerability to climate impacts. Direct damage to playing fields resulted from extreme climate events. Indirect impacts include higher injury risks, interrupted and/or cancelled competitions, insurance risks, plus higher operating and capital costs. Adapted management was evident for water resources, playing turf, and organisational policies. Practical Implications: Provides insights into the changing practice of sport management at the community-level. Research Contribution: The results challenge the assumption that climate is a static and benign resource for sport. This study demonstrates impacts of climate extremes on sport in the northern and southern hemispheres, the potential for adapting sport management practices, and developing resilience.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".