Every river tells a story: the Don River (Toronto) and the Los Angeles River (Los Angeles) as articulating landscapes
Bibliographic record
Abstract
Case studies of the Don River in Toronto, and the Los Angeles River in Los Angeles, inform a discussion of governance of urban landscapes in North America. The analysis and discussion in the paper centres on the way urban environments are part of the governance of a complexity of globalized urban areas. The empirical base of the paper is a set of interviews conducted between 1995 and 1997 with government officials, environmental and social activists, and business people. The stories of the two rivers indicate that urban ecological politics may be hegemonic or anti-hegemonic, supportive of existing regulatory structures or counter-regulatory. We suggest that in Toronto, urban ecological politics is civic, whereas in Los Angeles, it is socio-economic. More than in Toronto, urban natures in Los Angeles have been linked with contentious social struggles around justice, culture and democracy. Copyright © 2000 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.028 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".