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Record W2162216687 · doi:10.1177/1206331204265991

Social Control and the Management of “Personal” Space in Shopping Malls

2004· article· en· W2162216687 on OpenAlexaff
John Manzo

Bibliographic record

VenueSpace and Culture · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsControl (management)Space (punctuation)SociologyPublic relationsFocus (optics)BusinessComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This study concerns behaviors of persons in planned spaces, namely, enclosed shopping malls, with particular interest in practices conducive to social control in those settings. As an ethnomethodological investigation, it addresses lived social experiences of actors in these sites and not only the deliberate agendas of security firms. Its focus is how persons orient to design elements of shopping malls—corridors, furniture, and flooring—and how these design features are construable as “players” in those contexts. The author examines how design can militate techniques of social-interactional management produced by actors themselves, sometimes unforeseen in architects’ or designers’ plans. This research suggests a broadening of the notion of social control beyond formal and informal human sources to include the physical features of spaces, spaces like shopping malls, and not only of prisons or similar institutions. This study advises that inanimate objects and the spaces that comprise them are informative for and relevant to behaviors of human interactants.

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)0.001
metaresearch head score (Gemma)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.012
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0000.001
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 head0.010
GPT teacher head0.279
Teacher spread0.270 · 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
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

Citations72
Published2004
Admission routes1
Has abstractyes

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