Bibliographic record
Abstract
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?” 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. 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. 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.062 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.093 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".