Greening the campus without grass: using visual methods to understand and integrate student perspectives in campus landscape development and water sustainability planning
Bibliographic record
Abstract
Water, once thought to be a limitless resource, is now recognised as finite and one of the most contentious, uncertain issues of the future. While universities are one of the largest users of potable water in the urban landscape, they are also widely regarded as leading sources for innovative solutions and showcasing ways forward in environmental sustainability. However, universities are also sites of power where decisions are often made without democratic engagement with their major stakeholders: students. This paper is part of a larger study that examines how undergraduate students in geography (n = 98) perceive and value water conservation initiatives on an urban Canadian campus. The research involved administering a survey focused on identifying barriers to participation in sustainability initiatives and how involvement in sustainable activities on campus can alter the landscape. An important part of the survey, and the focus of this paper, was to examine how participants evaluated and ranked photographs of prospective campus landscape images and how they perceived its value. This method offers a way forward from how the traditional expert-based mapping and development of the campus landscape can effectively incorporate student values. Emphasis is placed on the desirability of student-based involvement in the evaluation and mapping of future land use development on the campus. Implications of communication barriers between students and policymakers are discussed with suggestions as to how student values, identified through the use of alternative landscape imagery, can be integrated into traditional landscape development and campus planning. Recommendations are made as to how community mapping, which enables communities to share information through a mapping structure, can be utilised to facilitate unique, inclusive and sustainable landscape planning, and to help integrate future student-directed sustainability projects.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".