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Record W2008671291 · doi:10.1068/a38217

Uneven Environmental Management: A Canadian Comparative Political Ecology

2007· article· en· W2008671291 on OpenAlexaffabout
Maureen G. Reed

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsStewardship (theology)Valuation (finance)Corporate governancePoliticsEnvironmental governanceEnvironmental resource managementBiosphereWork (physics)Political ecologyProperty managementEnvironmental planningBusinessPolitical scienceEcologyGeographyEconomicsEngineering

Abstract

fetched live from OpenAlex

Contemporary researchers of environmental management argue for community-based approaches in which local circumstances, skills, and concerns are respected. However, relying on local capacity opens up the possibility of establishing highly uneven management practices. The purpose of this paper is to explore the roots and effects of uneven environmental management. I develop a conceptual framework that identifies key elements of regional environmental-management regimes and then use it to compare experiences in two areas designated as Canadian biosphere reserves in 2000: Clayoquot Sound, BC, and Redberry Lake, SK. Analysis reveals that differences in property instruments and civic sectors affect the institutional capacity of each locality, opening the door for private forms of environmental governance in Redberry Lake. To explain how property instruments and civic actors operate, I illustrate how processes associated with property exchange, reterritorialization, valuation, and planning work together to produce a relatively robust and public regime at Clayoquot Sound and a more private form of stewardship at Redberry Lake. In consequence, uneven environmental-management practices may take root and reinforce social inequalities across the two regions.

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.

How this classification was reachedexpand

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.010
GPT teacher head0.216
Teacher spread0.206 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2007
Admission routes2
Has abstractyes

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