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Record W2902529538 · doi:10.1111/cico.12339

Making Jerusalem “Cooler”: Creative Script, Youth Flight, and Diversity

2018· article· en· W2902529538 on OpenAlexafffund
Noga Keidar

Bibliographic record

VenueCity and Community · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoHebrew University of Jerusalem
KeywordsCreative cityElitePoliticsSociologyDiversity (politics)DestinationsCreative classPolitical economyCreative CitiesPolitical scienceEconomyEconomic geographyTourismCreativityLawEconomics

Abstract

fetched live from OpenAlex

The creative city approach, already one of the most popular urban development models in recent years, continues to spread to new destinations. When urban scholars explain how ideas become canon, including the particular case of the creative city approach, they usually focus on political–economic mechanisms, the role of global elite networks, and the interests of local economic growth coalitions. These explanations are insightful but miss the political–cultural projects that cities pursue concurrently to the creative city approach, two aims that sometimes reinforce each other and sometimes contradict. Using interviews and fieldwork, I follow the importation of the creative city approach to the contested city of Jerusalem, and argue that the drive to adopt the creative script cannot be explained only by political–economic forces, but also by the local political–cultural projects of preserving Jerusalem as a Zionist city. Moreover, I suggest three directions for interpreting the role of local forces in the adoption and translation of urban ideas.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.020
Scholarly communication0.0070.003
Open science0.0010.004
Research integrity0.0010.002
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 head0.229
GPT teacher head0.328
Teacher spread0.098 · 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 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

Citations13
Published2018
Admission routes2
Has abstractyes

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