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Neoliberalism and the Politics of Alternatives: Community Forestry in British Columbia and the United States

2006· article· en· W2031758409 on OpenAlexaboutno aff
James J. McCarthy

Bibliographic record

VenueAnnals of the Association of American Geographers · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsCommunity forestryPolitical scienceNeoliberalism (international relations)State (computer science)Public administrationPoliticsGovernment (linguistics)ForestryGeographyForest managementLaw

Abstract

fetched live from OpenAlex

Calls for community forestry on public forests grew in strength in both British Columbia and the United States during the 1990s, as part of a global movement touting the advantages of community control over centralized state administration of forests. Despite structural similarities, the trajectories of community forestry in the two locations diverged sharply, with community forests rapidly becoming a reality in British Columbia while similar proposals in the United States were blocked. This article explains these divergent trajectories by examining the differences in property relations, state institutions, stakeholder interests, and environmental social-movement strategies that led to nearly opposite outcomes in initially similar situations. It also analyzes community forestry in British Columbia relative to current debates over neoliberalism and alternative economies, arguing that detailed examination of such empirical examples demonstrates the utility of neoliberalism as an analytical concept.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.494

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0200.018
Scholarly communication0.0090.002
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.008
GPT teacher head0.237
Teacher spread0.229 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations224
Published2006
Admission routes1
Has abstractyes

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