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Record W4396572280 · doi:10.1007/978-3-031-55680-7_3

Urban Policy Modelling and Diversity Governance in Doha and Singapore

2024· book-chapter· en· W4396572280 on OpenAlexaff
Jérémie Molho

Bibliographic record

VenueIMISCOE research series · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDiversity (politics)Corporate governanceGeographyEconomic geographyEnvironmental planningPolitical scienceBusinessFinance

Abstract

fetched live from OpenAlex

Abstract The transnational circulation of policy ideas has been increasingly advanced as a significant factor in the fabric of local diversity policies. On the one hand, the circulation of managerial concepts such as diversity management has contributed to the rise of neoliberal urban diversity models; on the other, city networks, international organisations, and transnational civic movements are pushing forward progressive urban diversity agendas. This chapter aims to analyse the role of such processes of policy modelling in shaping urban diversity governance. It is based on fieldwork conducted in Doha and Singapore since 2018 and on the analysis of these cities’ policy documents. The chapter shows how transnationally circulating references and norms contribute to shaping local diversity governance frameworks and how both cities strive to position themselves as diversity governance models. I argue that their modelling strategies rely on the spatial and organisational compartmentalisation of distinct diversity frames. The chapter identifies four compartments in Doha and Singapore that correspond to distinct understandings of diversity and differentiated modelling strategies. This allows to minimise policy tensions, alleviate external critiques, and craft local experiments that can be projected as models on the world stage.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.626
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.115
GPT teacher head0.376
Teacher spread0.261 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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