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Record W2012125289 · doi:10.1089/eco.2011.0039

Experiences of Environmental Justice and Injustice in Communities of People Experiencing Homelessness

2011· article· en· W2012125289 on OpenAlexafffundabout
Kate Klein, Manuel Riemer

Bibliographic record

VenueEcopsychology · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsWilfrid Laurier University
FundersWilfrid Laurier University
KeywordsInjusticeEnvironmental justiceVulnerability (computing)Economic JusticeCriminologySociologyPsychologyEnvironmental ethicsPolitical scienceSocial psychologyLaw

Abstract

fetched live from OpenAlex

Abstract People experiencing homelessness have a particular vulnerability to environmental hazards, yet there is little attention paid to the issue of homelessness in environmental justice literature. The current study is a phenomenological inquiry into the experiences of environmental justice and injustice in a community of people who are experiencing homelessness. To understand how people who experience homelessness in the Waterloo Region, Ontario, community conceptualize and experience their environment in terms of cleanliness, healthfulness, safety, and justice, 12 semi-structured interviews were conducted with people experiencing absolute homelessness in Kitchener and Cambridge, Ontario. Analysis shows that although knowledge of systemic environmental injustices is all but absent in this community, particular phenomena do stand out as critical issues, such as substandard rooming houses, drugs and alcohol, litter and pollution, and the behavior of police officers and city officials. Analysis also unveiled a significant dissatisfaction with municipal decision-making processes .

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.008
Scholarly communication0.0020.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.318
Teacher spread0.280 · 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 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

Citations16
Published2011
Admission routes3
Has abstractyes

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