MétaCan
Menu
Back to cohort
Record W2041352576 · doi:10.1017/s1537592705510491

Brave New Neighborhoods: The Privatization of Public Space

2005· article· en· W2041352576 on OpenAlexaff
Warren Magnusson

Bibliographic record

VenuePerspectives on Politics · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical and Economic history of UK and US
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDemocracyPoliticsSpace (punctuation)Argument (complex analysis)Public spaceAsidePolitical scienceEmpowermentSociologyPolitical economyPublic administrationLaw and economicsLawEngineering

Abstract

fetched live from OpenAlex

Brave New Neighborhoods: The Privatization of Public Space. By Margaret Kohn. New York: Routledge, 2004. 256p. $85.00 cloth, $22.95 paper. In her first book, Radical Space: Building the House of the People (2003), Margaret Kohn analyzed the spatiality of early working-class activism in Italy and developed a sophisticated argument about the conditions for democratic empowerment. In this book, she shifts her attention to the United States. Aside from one chapter on the Wobblies, the focus is on the present. In both books, her emphasis is on the way in which the space for political engagement—“public space”—is constructed, sustained, controlled, or foreclosed. Her overarching theme is that theorists of democracy have paid far too little attention to the spatial conditions for democratic interaction. As she attempts to show in the present book, the privatization of public space—that is, the transformation of once-open downtowns into privately governed business improvement districts, the creation of privately governed gated communities in the suburbs and elsewhere, and the colonization of once-public space by private businesses—tends to insulate people from direct, physical encounters with people who are “different” or who may be attempting to persuade them to think otherwise about political issues. The trend toward privatization is particularly pronounced in the United States, and Kohn's concern here is to show us why we should be concerned about it, as democrats.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.040
GPT teacher head0.299
Teacher spread0.259 · 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

Citations6
Published2005
Admission routes1
Has abstractyes

Explore more

Same venuePerspectives on PoliticsSame topicPolitical and Economic history of UK and USFrench-language works237,207