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Record W7020071945

Jane Jacob and designing diversity: investigating gastronomic quarters and food courts of shopping malls and vitality of public spaces

2012· article· en· W7020071945 on OpenAlexaboutno aff

Bibliographic record

VenueNOVA (University of Newcastle Australia) · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsVitalityConsumption (sociology)Context (archaeology)Public spaceSustainable consumptionSpace (punctuation)Quarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

The production, sale and consumption of food have traditionally been central and integrated parts of the public realm. However, food spaces are generally designed as segregated domains and thus don’t connect with their urban context in an organic manner, or they are designed as privatised exclusive enclaves within shopping malls, thus having a detrimental impact on the vitality of surrounding public spaces. In recent years, a revival of food-centered spaces in cities around the world has tried to re-establish a sustainable connection between food and urban space. Jane Jacobs, in her famous book The Death and Life of Great American Cities, established the framework for analysis of dynamic diversity in cities. This paper investigates two models of food-centered space within the city—the gastronomic quarter of the city and the food court of the shopping mall—and establishes the differences and connections between the two environments. Using Jacobs’ theory of diversity, it analyses these spaces and evaluates their contribution to the vitality of the public realm. The intention is to highlight the need for further research into the field of food-centered urban space in order to create economically and socially sustainable architectural and urban design responses to the way in which food production and consumption integrates with the city.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.250

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 studies0.0000.000
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 head0.221
GPT teacher head0.256
Teacher spread0.035 · 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 designObservational
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
Published2012
Admission routes1
Has abstractyes

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