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Record W2138551706 · doi:10.1017/s0008423906309985

Bioregionalism and Civil Society: Democratic Challenges to Corporate Globalism

2006· article· en· W2138551706 on OpenAlexaffabout
Peter R. Mulvihill

Bibliographic record

VenueCanadian Journal of Political Science · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsYork University
Fundersnot available
KeywordsCivil societyGlobalismCarrDemocracyGlobalizationPolitical scienceSociologyPolitical economyLawEcologyPolitics

Abstract

fetched live from OpenAlex

Bioregionalism and Civil Society: Democratic Challenges to Corporate Globalism, Mike Carr, Vancouver: UBC Press, 2004, pp. viii, 344. Civil society and environmental activists have gained recognition as strong forces of resistance to a common adversary—globalization. Considering this commonality of purpose, one might expect their links to be extensive and profound. But, as Mike Carr argues in this new book, this is not necessarily the case, or at least not to date. Carr, a Vancouver-based educator and activist in the social justice, peace and ecology movements, argues that there is strong, even transformational potential in forging greater links between civil society and bioregionalism.

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)0.004
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0110.039
Scholarly communication0.0200.010
Open science0.0010.006
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0060.001

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 head0.027
GPT teacher head0.207
Teacher spread0.180 · 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

Citations4
Published2006
Admission routes2
Has abstractyes

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