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Record W3125041644 · doi:10.60082/0829-3929.1269

Making a Music City: The Commodification of Culture in Toronto’s Urban Redevelopment, Tensions between Use-Value and Exchange-Value, and the Counterproductive Treatment of Alternative Cultures within Municipal Legal Frameworks

2009· article· en· W3125041644 on OpenAlexaffvenueabout
Sara Ross

Bibliographic record

VenueJournal of Law and Social Policy · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsYork University
Fundersnot available
KeywordsCommodificationRedevelopmentValue (mathematics)Urban regenerationSociologyDiversity (politics)Inclusion (mineral)Political sciencePublic administrationEnvironmental ethicsSocial scienceEnvironmental planningEconomyLawEconomicsGeography

Abstract

fetched live from OpenAlex

Meaningful diversity and inclusion within today’s cities requires attention on many fronts, including that of city redevelopment strategies and policies. To that end, this article focuses on culture-led regeneration strategies—specifically, those of Toronto’s “Music City” initiative and “Creative City” strategy—and unpacks the mechanics of using culture and heritage as tools for redevelopment where their commodification can reveal the clash between divergent value interests that exist within spaces of culture in the city. Sustainable urban development must carefully account for these divergences to avoid the displacement and lack of equitable accounting of relationally vulnerable individuals, groups, (sub)cultures, and space. Counterproductive effects of culture-led redevelopment initiatives which have, despite themselves, wound up either dismantling or failing to curb the disappearance of the “culture” that served as their initial impetus, are an example of where urban governance and planning have not effectively engaged precarious groups and spaces in the city that are affected by redevelopment strategies and where exchange-value interests have overwhelmed use-value interests to their detriment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.096
GPT teacher head0.386
Teacher spread0.290 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations5
Published2009
Admission routes3
Has abstractyes

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