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Record W3181020552 · doi:10.13130/milanlawreview/15194

Comparative Legal Perspectives on Cultural Land Trusts for Urban Spaces of Culture, Community, and Art: A Tool for Counteracting Displacement

2021· article· en· W3181020552 on OpenAlexaffabout
Sara Ross

Bibliographic record

VenueeYLS (Yale Law School) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDisplacement (psychology)Legal cultureSociologyPolitical scienceEnvironmental ethicsLawPsychologyPhilosophy

Abstract

fetched live from OpenAlex

As cities redevelop and previously less desirable or marginalized portions of the city space are “retaken” by a city, areas that have provided affordable performance, rehearsal, and live/work space for the arts and culture sector are becoming increasingly less available for these uses. Focusing predominantly on the Canadian Civil Law and Common Law context with passing reference to other jurisdictions such as the US, Scotland, and the UK, this article explores techniques for managing the increased pressure on and increasingly rapid displacement of spaces of arts, culture, and community cultural wealth that is taking place in cities. To this end, in assessing newly adopted municipal and provincial cultural strategies that are intended to amplify and promote these same spaces that are being displaced as well as even more recent COVID-19 recovery plans for art and culture in cities, this article will narrow in on the potential application of the community-led cultural land trust structure.

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)0.013
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 score0.235
Threshold uncertainty score0.468

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0200.082
Scholarly communication0.0130.009
Open science0.0030.009
Research integrity0.0060.005
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 head0.049
GPT teacher head0.341
Teacher spread0.292 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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