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Record W4281786092 · doi:10.3390/socsci11060242

The “Problem” of Multispecies Families: Speciesism in Emergency Intimate Partner Violence (IPV) Shelters

2022· article· en· W4281786092 on OpenAlexaffabout
Sarah May Lindsay

Bibliographic record

VenueSocial Sciences · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDomestic violenceRelocationCriminologyResource (disambiguation)SociologyPublic relationsPsychologyPoison controlPolitical scienceSuicide preventionEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

When a woman seeks emergency shelter from an abusive relationship, she may bring her children but rarely companion animals. Through a Critical Animal Studies (CAS) lens, this article qualitatively analyzes in-depth interviews with shelter workers in Ontario, Canada, exploring the place of multispecies families in intimate partner violence (IPV) shelters. The findings indicate that companion animals are viewed as problematic, as obstacles to their clients’ safe relocation, falling outside the scope of IPV shelters (who rarely take a co-sheltering approach), and as potential strains on an already resource-stretched social institution. Addressing a gap in the literature about the effects of companion animal policies in social housing on clients and staff, the results are relevant to social service providers and policymakers working with multispecies families, including insights about women and children’s reactions to separation from companion animals, contradictions in related policies, and institutional priorities. The article concludes that multispecies families are poorly accounted for in the IPV shelter system and suggests that researchers and shelters should collaborate with their communities to advocate for resources and policies that accommodate families with companion animals.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.788

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 studies0.0010.001
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 head0.031
GPT teacher head0.369
Teacher spread0.338 · 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 designBench or experimental
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

Citations2
Published2022
Admission routes2
Has abstractyes

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