Climate change and Canadian community grass-based sport fields
Bibliographic record
Abstract
This study examines adaptations being completed by those maintaining municipal grass-based sports fields during times of climate change. A total of sixteen in-depth interviews were conducted from May to July 2016. A semistructured interview guide was utilized. The questions aimed to determine the awareness and impact of changing weather conditions or the problem of climate change, including the priority of the problem, and weather information sources. As well, the questions sought understandings on the strategies for adapting/mitigating for the issue of climate change. All interviews were audio-taped and transcribed verbatim for analysis. Content analysis of the data was utilized, and, specifically, all data were analyzed and categorized by topics, key themes, and/or central questions offered for interpretation. The interview participant selection involved purposeful sampling. The participants held a variety of key employment positions that included directors, senior managers, managers, and key maintenance personnel of departments such as Parks and Recreation, Open Spaces, Parks Operations, Community Facilities, Program Standards, and Parks Maintenance. Each participant was provided a code name, including Participant–1 (P–1) to Participant–16 (P–16). The participants were from the area known as the Golden Horseshoe region, ranging from Niagara Falls to Toronto, Ontario, Canada. Municipal workers are making adaptations to maintain grass-based sports fields due to changing weather conditions that could be related to climate change. These impacts involved two key areas: seasons that were not normal as well as new pests and disease. The participants outlined their adaptations, which involved five key sport field maintenance themes. Importantly, a central question was raised as a debate topic for establishing the future directions for grass-based sports fields in times of climate change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".