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Record W2278863307 · doi:10.1177/0263775815599312

Enjoyable life: Planning, amenity and the contested terrain of urban biopolitics

2015· article· en· W2278863307 on OpenAlexaffabout
Ted Rutland

Bibliographic record

VenueEnvironment and Planning D Society and Space · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical and Economic history of UK and US
Canadian institutionsConcordia University
Fundersnot available
KeywordsAmenityBiopowerNormativeAgency (philosophy)PoliticsSociologyPopulationPolitical scienceUrban planningPublic administrationGeographySocial scienceLaw

Abstract

fetched live from OpenAlex

This article explores the connections between urban planning and a particular form of biopolitics. These connections are investigated by looking at the emergence of “enjoyment” as a planning concern in late 1960s Halifax, Nova Scotia. This new concern, the article suggests, emerged as a result of a political struggle involving activist groups, a newly formed state agency, and elements of the post-World War II political establishment. Wedded to this concern were two essential planning policies: the promotion of “amenity” (especially in the downtown) and the introduction of structured “citizen involvement” in planning decisions. Together, these two policies inaugurated a new form of planning and biopolitics. The promotion of amenity aimed to create a more enjoyable life through the alteration of prevailing conditions of life, while citizen involvement routed planning decisions – including the precise meaning of amenity – through “liberal” practices of government. Most importantly, the new policies were shaped by the enactment of normative divisions within the population, a characteristically biopolitical effect. The result of these divisions was a highly unequal process of citizen involvement and a correspondingly uneven terrain of enjoyment: a terrain whose development and use would provide enjoyment for “normative” populations, while leaving “pathological” populations unaffected or worse.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.037
GPT teacher head0.251
Teacher spread0.214 · 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 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

Citations23
Published2015
Admission routes2
Has abstractyes

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