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Record W2598666118 · doi:10.36939/cjur/vol25no2/art48

Out, Damned Spot: Socio-economic Hygienic Practices of Business Improvement Districts

2016· article· en· W2598666118 on OpenAlexaffvenue
Matthew D. Sanscartier, James Gacek

Bibliographic record

VenueCanadian journal of urban research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsCarleton University
Fundersnot available
KeywordsSocioeconomic statusIdeologyNaturalizationGenocideSociologyEnforcementHygienePoliticsCriminologyPolitical scienceLawCitizenship

Abstract

fetched live from OpenAlex

In this paper, we propose and develop the concept of “socio-economic hygiene” to denote the ways in which neoliberal Western urban space is spatially regulated and re-oriented towards consumption in a way that reinforces social exclusion. By connecting genocide literature with that of urban sociology, we parallel “socioeconomic hygiene” with “racial hygiene” in order to highlight similar sociological motivations and spatial tactics within both regimes. This includes the enforcement of a binary within which dominant and subordinate identities are constructed; the naturalization of the “Other” either through biology (in the case of racial hygiene) or place (in the case of socio-economic hygiene); and the micro-political enforcement of ideological genocidal/neoliberal tenets “on the ground,” translating ideology into practical social cues. We conclude by tracking how sociological strategies of “hygiene” have moved from racial and biological features to features of place and socioeconomic status, and how BIDs, resembling genocidal states in certain ways, use these strategies to continually justify their own existence.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.112
GPT teacher head0.402
Teacher spread0.289 · 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.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations3
Published2016
Admission routes2
Has abstractyes

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