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Record W2070785462 · doi:10.7202/1017516ar

Common Ownership and Equality of Autonomy

2013· article· en· W2070785462 on OpenAlexvenueaboutno aff
Anna di Robilant

Bibliographic record

VenueMcGill Law Journal · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsAutonomyContext (archaeology)Common ownershipCommon-pool resourceVariety (cybernetics)EthosCommon lawBusinessNormativeResource (disambiguation)Public economicsLaw and economicsEconomicsPolitical scienceLawMarket economyGeographyMicroeconomics

Abstract

fetched live from OpenAlex

In recent years, common ownership has enjoyed unprecedented favour among policy-makers and citizens in the United States, Canada, and Europe. Conservation land trusts, affordable-housing co-operatives, community gardens, and neighbourhood-managed parks are spreading throughout major cities. Normatively, these common-ownership regimes are seen as yielding a variety of benefits, such as a communitarian ethos in the efficient use of scarce resources, or greater freedom to interact and create in new ways. The design of common-ownership regimes, however, requires difficult trade-offs. Most importantly, successful achievement of the goals of common-ownership regimes requires the limitation of individual co-owners’ ability to freely use the common resource, as well as to exit the common-ownership arrangement. This article makes two contributions. First, at the normative level, it argues that common ownership has the potential to help foster greater “equality of autonomy”. By “equality of autonomy”, I mean more equitable access to the material and relational means that allow individuals to be autonomous. Second, at the level of design, this article argues that the difficult trade-offs of common-ownership regimes should be dealt with by grounding the commitment to equality of autonomy in the context of specific resources. In some cases, this resource-specific design helps to minimize or avoid difficult trade-offs. In hard cases, where trade-offs cannot be avoided, this article offers arguments for privileging greater equality of autonomy over full negative freedom.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.247
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.

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

Citations1
Published2013
Admission routes2
Has abstractyes

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