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Record W4323044594 · doi:10.1080/13698230.2023.2185382

Common-pool resources and democracy

2023· article· en· W4323044594 on OpenAlexafffundabout
Spencer McKay

Bibliographic record

VenueCritical Review of International Social and Political Philosophy · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCommonsDemocracyPluralism (philosophy)DeliberationLaw and economicsSociologyCommon-pool resourceArgument (complex analysis)Deliberative democracyPolitical scienceValue (mathematics)IndigenousPower (physics)TreatyPolitical economyLawPoliticsEconomicsEpistemology

Abstract

fetched live from OpenAlex

The commons is frequently taken to be a model of democracy. Yet, the problems of overuse and enclosure that plague common-pool resources suggest that democratic norms of inclusion, equality, and pluralism may not be realized in practice. Existing contractarian accounts of the democratic value of the commons tend to assume equality of power and clear boundaries between users and non-users. Alternative accounts that emphasize practices of commoning assume a shared social identity that appears incompatible with pluralism. These accounts provide little insight into how common-pool resource regimes marked by inequalities of power might be democratized for the long-term. In contrast, I argue that the idea of treaty can serve as the basis for a democratic theory of the commons that justifies exclusion of those who would oppress marginalized groups, facilitates polycentric governance, and creates opportunities for deliberation and contestation. I illustrate the argument with reference to disputes over fisheries between the Mi’kmaq, non-Indigenous peoples, and the Canadian state.

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)0.008
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.103
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0080.073
Scholarly communication0.0090.008
Open science0.0010.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.053
GPT teacher head0.395
Teacher spread0.341 · 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 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

Citations3
Published2023
Admission routes3
Has abstractyes

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