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Record W4251436020 · doi:10.1080/738552351

Every river tells a story: the Don River (Toronto) and the Los Angeles River (Los Angeles) as articulating landscapes

2000· article· en· W4251436020 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Environmental Policy & Planning · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsYork University
Fundersnot available
KeywordsHegemonyPoliticsCorporate governanceDemocracyUrban politicsGovernment (linguistics)SociologyEnvironmental governancePublic administrationPolitical scienceLawManagement

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.006
GPT teacher head0.214
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it