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Record W4299925625 · doi:10.11116/9789461664952

Mobs and Microbes

2022· book· en· W4299925625 on OpenAlexaff

Bibliographic record

VenueLeuven University Press eBooks · 2022
Typebook
Languageen
FieldSocial Sciences
TopicGlobal Maritime and Colonial Histories
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsUrbanismArchitectureSanitationUnrestGovernment (linguistics)PoliticsPolitical scienceEconomyPolitical economySociologyHistoryEngineeringEconomicsLawArchaeology

Abstract

fetched live from OpenAlex

Markets and market halls have always been more than about trade and nourishment. A detailed look at the histories of marketplaces provides evidence of the public health concerns they faced, as well as the social commotion, mobilization and, at times, unrest they hosted. This edited volume reappraises the market hall, examining both its architectural and its social and political significance. Focusing on how these buildings embodied transformations in architecture and urbanism from the mid-nineteenth century until the age of COVID-19, Mobs and Microbes situates market halls at the intersection of civic order and public health. Central to this are advances in sanitation and hygiene. These radical interventions also mediated conflicting interests. Through their rational designs, market halls intertwined government policies and regulations, which formalized, controlled and literally imposed order. Additionally, markets served as demonstration grounds for community-led mobilization efforts. With case studies spanning North America, Europe, Asia, India and Africa, this edited volume provides a global perspective on covered market halls across many disciplines, including architecture, history of art and architecture, landscape architecture, food studies and urban history.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.036
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0070.005
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0360.009

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 head0.017
GPT teacher head0.213
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2022
Admission routes1
Has abstractyes

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