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Record W2886905957 · doi:10.1515/til-2018-0028

Indigenous Peoples, Political Economists and the Tragedy of the Commons

2018· article· en· W2886905957 on OpenAlexaffabout
Michel Morin

Bibliographic record

VenueTheoretical Inquiries in Law · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsTragedy of the commonsIndigenousCommonsPoliticsPopulationEnvironmental ethicsNatural resourceLaw and economicsSociologyPolitical economyPolitical scienceLawEcology

Abstract

fetched live from OpenAlex

Abstract In “The Tragedy of the Commons,” Garrett Hardin implicitly moved from bounded commons — a pasture or a tribe’s territory — to the case of boundless commons — the ocean, the atmosphere and planet Earth. He insisted on the need for imposing limits on the use of these resources, blurring the difference between communal property and open access regimes. The success of his paper is due in great measure to his neglect of economic, scientific, legal and anthropological literature. His main lifelong focus was on limiting population growth. He could have avoided the conceptual confusion he created by turning to well-known political economists such as John Locke and Adam Smith or, for that matter, jurists, such as Blackstone. Instead, he simply envisioned indigenous lands as an unbounded wilderness placed at the disposal of frontiersmen. Though he eventually acknowledged the existence of managed commons, he had little interest in community rules pertaining to resource exploitation. For him, these were simply moral norms which inevitably became ineffective after a community reached a certain level of population. He also took economists to task for failing to include in their analysis the true environmental and social costs of public decisions. Still, the famous example of the indigenous people of Northeastern Quebec illustrates a shortcoming of his analysis: community members did not act in total isolation from each other. On the contrary, communal norms could prevent an overexploitation of resources or allow for the adoption of corrective measures.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.159
Threshold uncertainty score0.317

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.013
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.279
Teacher spread0.260 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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