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Urban Health and Healthy Cities Today

2020· reference-entry· en· W3089658816 on OpenAlex

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

VenueOxford Research Encyclopedia of Global Public Health · 2020
Typereference-entry
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsTypologyContext (archaeology)Order (exchange)Perspective (graphical)Promotion (chess)Health promotionSmart cityTheme (computing)Political scienceEconomic growthBusinessPublic relationsGeographyHealth careInternet privacyComputer scienceEconomicsPoliticsInternet of Things

Abstract

fetched live from OpenAlex

Abstract The authors of this article purport that for current understanding of Healthy Cities it is useful to appreciate other global networks of local governments and communities. In a context where the local level is increasingly acknowledged as decisive in designing and implementing policies capable of tackling global threats such as climate change and their health-related aspects, understanding how thousands of cities across the world have decided to respond to those challenges appears essential. Starting with the concept of “healthy cities” in the 1980s, the trend toward promoting better living conditions in urban settings has rapidly grown to encompass today countless “theme cities” networks. Each network tends to focus on more or less specific issues related to well-being and quality of life. These various networks are thus not limited to more or less competing labels (Healthy Cities, Smart Cities, or Inclusive Cities, for instance), but entail significant differences in their approaches to the promotion of health in the urban context. The aim of this article is to systematically typify these “theme cities.” A typology of “theme cities” networks has several objectives. First, it describes the health aspects that are considered by the networks. Are they adopting a systemic perspective on all health determinants, such as Healthy Cities, or are they focusing on “hardware” determinants like Smart Cities? Second, it highlights the key characteristics of the networks. For instance, are they pushing for technological solutions to health problems, like Smart Cities, or are they aiming at strengthening communities in order to mitigate their detrimental effects, like Creative Cities? Third, the typology has the potential to be used as an analytical tool, for example, in the comparison of the results obtained by different types of networks in urban health issues. Finally, the typology offers a tool to enhance both transparency and participation in the policymaking process taking place when selecting and engaging in a network. Indeed, by clarifying the terms of the debate, decisions can be made more explicit and achieve a greater level of congruence with the overall objectives of the city. Indeed, Healthy Cities today need to make alliances with other theme networks, and this typology gives the keys to find which networks are the “natural best allies,” avoiding mutually harmful antagonisms. In that sense, the typology developed should be of interest to any actor involved in health promotion at the city level, whether in an existing “theme cities” policy process or as willing to participate in such a program, and to scholars interested in better understanding the main drivers of “theme cities” networks, a rapidly growing field of study.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
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 head0.335
Teacher spread0.265 · 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