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Neoliberalization and Its Geographic Limits: Comparative Reflections from Forest Peripheries in the Global North

2012· article· en· W2115444589 on OpenAlexaffabout
Roger Hayter, Trevor J. Barnes

Bibliographic record

VenueEconomic Geography · 2012
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsNeoliberalism (international relations)PoliticsEconomic geographySociologyPolitical economyEconomyPolitical scienceGeographyEconomicsLaw

Abstract

fetched live from OpenAlex

abstract Recently, a number of economic geography studies have emphasized that when neoliberalism is grounded in particular places, it takes on hybrid forms, a result of local contingencies that are found at those sites. This article contributes to this literature by explicating the processes by which hybridization occurs by drawing on a comparative study of neoliberalism in three contemporary forest‐based regions in the Global North: British Columbia, Canada; Tasmania, Australia; and the North Island, New Zealand. A key term for us is geographic limits, by which we mean regionally specific constellations (assemblages) of institutional and material forms that resist; hybridize; or, at junctures, even offset neoliberalism with alternative agendas. In turn, our idea of geographic limits is derived from our larger conceptual framework that integrates Anna Tsing's (2005 ) concept of friction with the notion of remapping and a four‐leg stakeholder model that consists of different, albeit overlapping, institutional agencies that represent the political, the industrial, the environmental, and the cultural. These institutions provide the animus for a remapping that variously implements, modifies, and occasionally counters neoliberalism.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0060.017
Scholarly communication0.0040.005
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.237
Teacher spread0.212 · 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

Citations35
Published2012
Admission routes2
Has abstractyes

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