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Record W626266070

The Cultural Turn in Municipal Planning

2009· dissertation· en· W626266070 on OpenAlexaboutno aff
Jason F. Kovacs

Bibliographic record

VenueUWSpace (University of Waterloo) · 2009
Typedissertation
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsCultural turnTurn (biochemistry)BusinessPolitical scienceEnvironmental planningSociologyGeographyChemistrySocial science
DOInot available

Abstract

fetched live from OpenAlex

Urban theorists and policy makers have begun to re-evaluate the importance of culture in urban development models. Culture is now widely viewed as a critical factor in the economic and social health of cities. Notions of creativity and the growing recognition of the role that culture-rich environments can play in attracting the “creative class,” are being partly expressed in the widespread adoption of urban cultural planning strategies. Cultural planning is commonly defined as the identification (mapping) and leveraging of cultural assets to support local community and economic development. It is also often explained as a “cultural approach” to municipal planning, an approach that entails effective cross-departmental and cross-sectoral collaboration in the implementation of strategic goals outlined within the cultural plan. A literature has been written on the potential of cultural planning by leading experts in the cultural policy field, especially from Australia and Britain. However, there has been a noticeable lack of critical research on this cultural development approach by scholars in Canada, where cultural planning is a relatively new and emerging municipal activity. This dissertation examines the policy and planning scope of the increasingly popular yet under-explored “municipal cultural planning” movement in Ontario, Canada. 
\nResearch began with a thorough review of the cultural planning literature. Cultural planning deficiencies and research gaps identified in the international literature were subsequently addressed through an analysis of all ten existing cultural plans in Ontario’s mid-size cities. The analysis of plans was complemented by thirteen in-depth interviews with municipal staff responsible for overseeing the development and implementation of cultural plans. Aside from addressing the interpretations of and rationales for municipal cultural planning, the information derived from document analysis and interviews was used to address four important issues that have been either ignored or only addressed in a cursory way in the literature: the nature and actual extent of community consultation and cultural mapping in the cultural plan development stage; the accuracy of the growing arts policy labelling of cultural planning abroad as it applies to municipal cultural planning; the relationship between cultural planning and its conceptual roots in urban planning; and the outcomes of the cultural planning strategy. 
\nIt was found that the development of cultural plans involved substantial community input, albeit not through the participatory “cultural mapping” process that is often claimed to be the preliminary step of cultural planning. In addition, it was observed that the increasingly common charge, particularly from Australia, that cultural plans are overly focussed on traditional arts sector concerns was not found to be the case with most cultural planning initiatives in Ontario. Further, while a strong urban development and planning-oriented basis has been used to differentiate cultural planning from traditional arts policy, the scope of cultural planning concerns in the sphere of urban planning practice was observed to be, with some noticeable exceptions, fairly superficial. However, this research also found that the strategic objectives outlined within cultural plans, which address a broad range of policy and planning activities related to cultural and community development, were generally being implemented and were effecting change.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.893
Threshold uncertainty score0.689

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)0.0000.000

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 head0.030
GPT teacher head0.268
Teacher spread0.238 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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