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Record W2345020187 · doi:10.1111/1468-2427.12339

Urban Political Ecologies and Children's Geographies: Queering Urban Ecologies of Childhood

2016· article· en· W2345020187 on OpenAlexaff
Laura Shillington, Ann Marie F. Murnaghan

Bibliographic record

VenueInternational Journal of Urban and Regional Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of WinnipegConcordia UniversityJohn Abbott College
Fundersnot available
KeywordsQueerSociologyPoliticsConversationRomanceGender studiesPolitical ecologySocial sciencePsychologyPolitical sciencePsychoanalysis

Abstract

fetched live from OpenAlex

Abstract This article focuses on the material and discursive constructions of nature and children in the city. While dominant representations and idealizations of nature and childhood depend on the binary logic of the nature/culture and rural/urban divide, there is also a simplification and romanticization of nature in children's geographies and a lack of children and their spaces in urban political ecology. We argue that children and nature in cities need to be removed from a binary model of being and attended to in more nuanced ways in urban political ecology and children's geographies. In this regard, we suggest that both nature and children in cities need to be queered. We need to ask how the production of urban spaces (re)creates particular romantic and idealized relations with natures that reify the binaries between nature/culture, and male/female through a heteronormative framework. The purpose of this article is to bring the critical nature–society theories of urban political ecology into conversation with work in children's geographies that explores the ‘nature' of childhood, and in doing so queer the relationship between children and nature. Drawing on research on queer ecologies, and queered childhoods, we aim to provide a framework to rethink and queer both nature and children in cities.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.053
GPT teacher head0.383
Teacher spread0.330 · 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.

Study designObservational
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

Citations24
Published2016
Admission routes1
Has abstractyes

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