Neighbouring with Messy Landscapes : Examining the Appreciation Towards Biodiverse Landscapes in Vancouver
Bibliographic record
Abstract
I would like to reflect on the significance of acknowledging the traditional and unceded territory of the Musqueam First Nation. It’s important to consider our relationship with the land and how we interact with it. The choices we make about our urban landscapes are far from arbitrary. Instead, they reflect larger cultural, ecological, and social forces that shape our relationship with the natural world around us. By considering the plantings we see every day, we can begin to unravel the complex layers of meaning and intention that underpin these seemingly simple decisions. Public preference for neat and orderly landscapes, influenced by the Picturesque landscape in the 18th century, puts pressure on maintenance and creates resistance to biodiverse designs in public spaces, despite the growing awareness of the importance of healthy and resilient green spaces. This cultural practice, although well-intended, often creates a static landscape that neglects thedynamic ecological processes within. My thesis project is a journey of exploring a biodiverse boulevard running through the neighborhood that serves as a tangible reminder of our interconnectedness with the natural world. It provides a space for us to reconnect with the rhythms of the earth, fostering a sense of rootedness and belonging both ecologically and culturally.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".