The Nature of Cities: Perspectives in Canadian Urban Environmental History
Bibliographic record
Abstract
Capilano Canyon seems in a realm quite separate from Vancouver. Steep rock walls, mossy and wet; rapids and pools tracing a thin line from snowpack to ocean; ferns and Douglas firs—all remote from the suburbs that surround them. To descend into the park is to pass between two worlds; from one of humans, to another of nature. But traces within the park undermine this distinction. Cedar stumps display notches made by loggers, and in a remote corner a timber railway rots quietly away. These testify to Vancouver’s history of timber-cutting, when the "forest vanished and up went the city."1 Around a bend in the river, Cleveland Dam’s concrete bulk suddenly appears, storing and diverting the river, annexing it to Vancouver’s water system. Today, though, loggers and engineers have been displaced by strollers and tourists, catered to by gentle paths and a bus parking lot. The park testifies to the evolving place of nature within a Canadian city.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.024 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".