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Record W2116065478 · doi:10.1017/s0008423906329964

Sustainability and the Civil Commons: Rural Communities in the Age of Globalization

2006· article· en· W2116065478 on OpenAlexaffabout
Roger Epp

Bibliographic record

VenueCanadian Journal of Political Science · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCommonsSustainabilityGlobalizationPopulationPoliticsCivil societyPolitical scienceEconomic growthLivelihoodEconomyDevelopment economicsGeographyAgricultureEconomicsSociology

Abstract

fetched live from OpenAlex

Sustainability and the Civil Commons: Rural Communities in the Age of Globalization , Jennifer Sumner, Toronto: University of Toronto Press, 2005, pp. viii, 179. As we are continually reminded, Canada is now an overwhelmingly urban country. Mythic vastness notwithstanding, most of its people and certainly its mobile “creative class,” presumed driver of the knowledge economy, live in major cities, whose policy requirements have captured a good deal of national attention in the past decade. By contrast, rural Canada has been reduced to the status of the space in-between. Its resource-based communities and livelihoods—farming, fishing, forestry—live with the downward price pressures of global commodity trade as well as the most intractable trade disruptions. Its public services and social infrastructure have been diminished. Aside from pretty places that have become recreational or residential enclaves, its population typically is declining and aging. Its widespread sense of abandonment so far has generated only inchoate, perhaps incoherent political responses. Meanwhile, the growing consensus among newspaper editorialists and think-tank policy specialists is that “dependent” and “unsustainable” rural Canada has been subsidized long enough for sentimental reasons at the expense of real needs elsewhere.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.226
Teacher spread0.213 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations19
Published2006
Admission routes2
Has abstractyes

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