Culture, Science, and Activism in Florida Lawn and Landscape Fertilizer Policy
Bibliographic record
Abstract
Every county and municipality in Florida can adopt its own unique ordinance regulating the fertilization of lawns and landscapes. With increased concern for eutrophication to state waterbodies, many have chosen to implement seasonal fertilizer restrictive periods prohibiting the application of nitrogen and phosphorus fertilizers, typically during the rainy summer months. These fertilizer “blackout” policies have been the subject of controversy among environmental activists, university scientists, and policy decision makers, with their efficacy being called into question. A Foucauldian discourse analysis was undertaken to trace the dynamics of the controversy, and survey research was conducted with Florida residents and with Florida decision makers to compare their lawncare maintenance practices, sentiments surrounding turfgrass, their trust in landscape science, as well as their awareness of policy in the city or county in which they reside. Differences were found between the two populations in terms of how many respondents fertilized, used automated irrigation systems and hand-pulled weeds. Although both populations had very neutral sentiments around turfgrass with no significant differences, Florida decision-maker respondents had a higher mean response for trust in landscape science. Only 32% of Florida resident respondents were able to accurately identify if their city or county had a blackout ordinance, compared with 81% of decision-maker respondents. Increasing civic science may be the best way for reducing this discrepancy, while also giving power to citizens in environmental policy adoption.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".