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Record W3216388622 · doi:10.24135/ijara.vi.670

Common notions and composite collaborations: Thinking with Spinoza to design urban infrastructures for human and wild cohabitants

2021· article· en· W3216388622 on OpenAlexfundaboutno aff
Sue Ruddick

Bibliographic record

VenueInterstices Journal of Architecture and Related Arts · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of TorontoUniversity of AucklandYork UniversityAuckland University of Technology, New Zealand
KeywordsTemporalitiesConstruct (python library)ChoreographyWildlifeSociologyEnvironmental ethicsAestheticsArchitectural engineeringUrban designGeographyArchitectureEcologyPolitical scienceComputer scienceArtLawVisual artsEngineeringArchaeologyPhilosophyBiology

Abstract

fetched live from OpenAlex

This paper explores the ways in which we might construct urban environments that are responsible to the needs of more than just human cohabitants. Drawing on Spinoza’s common notion and attentive to the possibilities of socio-natures that both construct and respond to the habitat needs of urban wildlife, I look at how urban design and wildlife habitat might be thought and planned together as a human/non-human composite, invoking a complex spatial and temporal choreography which serves divergent needs. Drawing on examples of urban design in Toronto, Canada, this paper offers a way to think of the city as a composite body in Spinoza’s terms, to become open to an awareness of the city as a composition of forces—a choreography of bodies that are constantly interweaving and overflowing imagined boundaries, struggles that are fought as much over time as space, the accommodation of the temporalities and spatialities of other life processes, other rhythms and cycles that would, without a recalibration, sync uneasily with the pacing and spacing of human requirements.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.874

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 studies0.0010.000
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 head0.019
GPT teacher head0.305
Teacher spread0.286 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueInterstices Journal of Architecture and Related ArtsSame topicGeographies of human-animal interactionsFrench-language works237,207