Stakeholder feedback on perspective renderings indicate broad support for no-mow management of campus greenspaces
Bibliographic record
Abstract
No-mow management of greenspaces is becoming a popular intervention to conserve biodiversity and balance other human functions (e.g. aesthetic qualities, accessibility) through integrating the concept of “cues to care” (CTC). Here, visible signs of human intention and maintenance were featured by varying degrees of CTC, where three “no-mow” design approaches were created for greenspaces on a university campus. Next, we surveyed a cross-section of campus stakeholders to provide insight into how these no-mow options were perceived. There was broad support for no-mow management of campus greenspaces, with a design centred around a matrix of no-mow “islands” within a mowed lawn finding the most support across each of the three sites. A design composed of a more extensive no-mow patch with a well-defined border, and an no-mow patch featuring a mowed bisecting linear path were selected less frequently as preferred designs. Thematic analysis of qualitative comments revealed that no-mow designs were preferred based on higher aesthetic quality, perceived human use of the space and accessibility, and the value for biodiversity conservation. We found demographics, site familiarity, and the geometry of patch features were likely contributors to the social acceptance of no-mow design, where a moderate degree of CTC out-performed alternative management options. Affiliation, gender, and age demographics showed students who identified as women and under the age of 30 were most responsive to no-mow designs compared to high-frequency mowing. No-mow management was more strongly preferred for unfamiliar sites, as compared to familiar sites. The curvature of no-mow island features were also preferred over linear mowed strips, supporting a fundamental human preference for more-natural geometry. Findings demonstrate that designed greenspaces that facilitate human uses, feature moderate CTC, and provide habitat may improve public perceptions, and thus the uptake of no-mow management.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".