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Record W2732830682 · doi:10.1080/0966369x.2017.1347559

Homelessness, nature, and health: toward a feminist political ecology of masculinities

2017· article· en· W2732830682 on OpenAlexafffund
Jeff Rose, Corey W. Johnson

Bibliographic record

VenueGender Place & Culture · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsSociologyEthnographyGender studiesMasculinityPoliticsPolitical ecologyNarrativeContext (archaeology)Social ecologyEcologyPolitical scienceGeographyAnthropology

Abstract

fetched live from OpenAlex

Engaging with feminist political ecology and leveraging experiences from a 16-month critical ethnography, this research explores ways in which masculinities served as both a rationale and an outcome of men facing homelessness living in the margins of an urban municipal public park – a space known as ‘the Hillside.’ Ethnographic narratives point to Hillside residents making their home in nature, connecting experiences in nature with various masculinities, and the gendered eschewing of social services. These portrayals further highlight the perceived feminization of social services within a context of rapidly neoliberalizing urban environments, and illustrate the ways participants positioned and engaged with social services. Entanglements of health and nonhuman nature prompt a feminist political ecological engagement with masculinity. Experiences from the Hillside add textured richness to discourses concerning the ways in which contemporary landscapes are constructed, perceived, experienced, and co-constituted through and with gender.

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)0.008
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0140.054
Scholarly communication0.0100.007
Open science0.0010.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.114
GPT teacher head0.444
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 source (direct Gemma or distilled Codex), 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

Citations23
Published2017
Admission routes2
Has abstractyes

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