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Record W4403807700 · doi:10.1017/cjlj.2024.11

The Liberal Case for Community Land Trusts

2024· article· en· W4403807700 on OpenAlexaboutno aff
Marc Goëtzmann

Bibliographic record

VenueCanadian Journal of Law & Jurisprudence · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicProperty Rights and Legal Doctrine
Canadian institutionsnot available
FundersEdmond J. Safra Center for Ethics, Harvard UniversityUniversité François-RabelaisTel Aviv University
KeywordsLaw and economicsLawPolitical scienceSociology

Abstract

fetched live from OpenAlex

Abstract This paper argues that community land trusts (CLTs) can be part of a liberal housing policy from both an economic and a legal point of view. In this, I depart from the defense of community land trusts based on the so-called ‘decommodification’ of housing. First, from an economic point of view, CLTs are a comparatively better option than other traditional policies, such as rent control, once the political complexity of housing policies is considered. Second, fostering autonomy and the capacity for self-authorship requires that individuals be able to choose from a diverse set of valuable institutional designs; CLTs correspond to that ideal and bring a unique nuance to the existing options within common-interest communities. Additionally, I look at the somewhat recent legal innovations that brought CLTs to Canada and continental Europe to show that the institutional flexibility of CLTs allows them to support different visions of self-realization through ownership.

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)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.023
Scholarly communication0.0080.005
Open science0.0010.007
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0070.000

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 head0.050
GPT teacher head0.337
Teacher spread0.288 · 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 designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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