Measuring ecological grief to guide inclusive urban forest management
Bibliographic record
Abstract
Aligning city planning decisions with inclusive and current preferences requires (re)examining urbanites’ relationships with their greenery. For example, Vancouver, Canada cultivates broadleaf street trees (reflecting visions of British colonizers), but does this choice reflect current residents? Ecological grief is an increasingly widely used concept that may help indicate today’s human–forest relationships. We surveyed residents of Metro Vancouver ( n = 600) and assessed how biophysical, geographical, social identity, and structural factors shape ecological grief. We used two Bayesian models and found that broadleaf and conifer declines evoke similar feelings of grief across all racial groups. We also found that birdwatching, running/walking on sidewalks, income, age, gender, and urbanness captured variations in grief. These results suggest that planting a greater variety of trees and enabling inclusive and meaningful interactions with urban forests may better serve today’s residents. Our study exemplifies how ecological grief can indicate relationships with nature and guide 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".