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Sustainable Governance of Common-Pool Resources: Context, Methods, and Politics

2003· article· en· W2108799600 on OpenAlexaff
Arun Agrawal

Bibliographic record

VenueAnnual Review of Anthropology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsCommonsCommon-pool resourceScholarshipProperty (philosophy)Context (archaeology)Corporate governanceSubject (documents)Law and economicsSovereigntyPoliticsProperty rightsSociologyCommon propertyField (mathematics)EpistemologyTragedy of the commonsPolitical scienceEnvironmental ethicsLawEconomicsComputer scienceHistoryManagementPhilosophy

Abstract

fetched live from OpenAlex

▪ Abstract This paper presents a critical assessment of the field of common property. After discussing briefly the major findings and accomplishments of the scholarship on the commons, the paper pursues two strategies of critique. The first strategy of friendly critique accepts the basic assumptions of most writings on common property to show that scholars of commons have discovered far more variables that potentially affect resource management than is possible to analyze carefully. The paper identifies some potential means to address the problem of too many variables. The second line of critique proceeds differently. It asks how analyses of common property might change, and what they need to consider, if they loosen assumptions about sovereign selves and apolitical property rights institutions. My examination of these questions concludes this review with an emphasis on the need to (a) attend more carefully to processes of subject formation, and (b) investigate common property arrangements and associated subject positions with greater historical depth.

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)0.035
metaresearch head score (Gemma)0.025
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: Review · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.006
Science and technology studies0.0040.046
Scholarly communication0.0120.010
Open science0.0020.007
Research integrity0.0020.003
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 head0.011
GPT teacher head0.305
Teacher spread0.293 · 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
GenreReview

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

Citations646
Published2003
Admission routes1
Has abstractyes

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