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Manufacturing Modernity: Cleaning, Dirt, and Neoliberalism in Chile

2006· article· en· W2092687084 on OpenAlexafffund
Patricia Tomic, Ricardo Trumper

Bibliographic record

VenueAntipode · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical and Social Dynamics in Chile and Latin America
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersUniversity of British ColumbiaUniversity of Toronto
KeywordsModernityNeoliberalism (international relations)DirtBackwardnessArticulation (sociology)Modernization theoryNexus (standard)DictatorshipSociologyHegemonyPolitical economyPolitical scienceEconomyDemocracyEconomic growthEconomicsLawEngineeringPolitics

Abstract

fetched live from OpenAlex

Since the coming to power in 1973 of the military dictatorship of General Augusto Pinochet, neoliberalism in Chile has been discursively tied to the goal of “modernizing” Chilean society. This discourse of modernization has relied for its articulation upon another important discourse, that of “cleanliness” as a marker of progress: clean spaces are seen as those of modernity whereas dirty spaces are taken to represent social and economic “backwardness”. In this paper, then, we explore how spaces which are considered emblematic of the modern economy—shopping malls, giant office complexes, university campuses—are maintained as clean spaces. However, in order for the discourse of cleanliness as modernity to work, it is crucial that the corridors of mobility—train routes, subways, city streets—which allow passage between these nodes of the modern economy also be maintained as sanitary spaces. The result is the construction discursively of an almost seamless nexus of hygienic spaces, one which stands in contrast to the dirty spaces of the “other” Chile. The paradox in all of this, however, is that the workers who are crucial to this project—janitors—have suffered greatly as a result of neoliberalism.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.274
Teacher spread0.263 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations18
Published2006
Admission routes2
Has abstractyes

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