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Record W7155462061

Experimenting the healthy city:Unpacking urban health experiments

2024· dissertation· en· W7155462061 on OpenAlex
Sabrina Huizenga

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEUR Research Repository (Erasmus University Rotterdam) · 2024
Typedissertation
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsCorporate governanceUrban planningEthnographyPublic healthPsychological resilienceUrban designLiminalityPower (physics)Urban studies
DOInot available

Abstract

fetched live from OpenAlex

In recent years, cities have increasingly been portrayed as dynamic centers of experimentation, driven by the need to find creative solutions to complex global challenges in times of increasing uncertainty. Experimentation as a mode of (urban) governance travels around the world and is taken up in myriad ways. This dissertation is about urban experimentation with a focus on the creation of the Healthy City. In this dissertation I studied urban health experiments in daily practice, asking: “How are urban health experiments constructed as a governance practice and what are the consequences thereof?” <br/>I engaged in a multi-sited ethnography to explore the concept of the Healthy City. This ethnographic approach involved following urban health experiments, i.e., ‘hanging out’, observing and analysing diverse urban health experiments: (a) urban (health) labs, (b) a resilience program, (c) a ‘workshop’ developing algorithmic governance for youth care, and (d) COVID-19 decision-making. Although very different in form, their collective objectives revolved around experimentally building a resilient and Healthy City. I examined them to understand the processes of construction and practices of urban health experiments that bring into being the ideal of a Healthy City, and to what consequences. As a result, this dissertation on experimenting the Healthy City takes on the form of five empirical chapters encompassing the Laboratory City, the Liminal City, the Resilient City, the Algorithmic City, and the Pandemic City.<br/>Crucially, I analysed these experiments as governance arrangements that reconfigure power relations and responsibilities between government, citizens, and other stakeholders. To this end, I highlight three components of the Healthy City: (1) how urban health experiments involve different processes of inclusion and exclusion, and thus prioritize some voices, knowledge and values over others; (2) how shifting responsibilities between governments and citizens can be empowering and energizing for some citizens, while proving precarious for others and can also background systemic underlying issues; and (3) how the dynamic interplay between each experiment and existing institutional contexts can inhibit free experimentation, thereby limiting the potential of alternative perspectives and practices to come to the fore in urban health experiments.<br/>This dissertation shows that far from being straightforward, the Healthy City and urban health experiments depend on the normative political interpretation given to them and their implementation, and therefore have consequences for how the Healthy City is given shape in practice. Crucially, this means that the Healthy City is plural. This thesis provides an insight into this plurality.<br/>

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.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.002
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 head0.070
GPT teacher head0.398
Teacher spread0.329 · 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