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Record W2902433023

Session G: Animals and Social Work: Examining barriers to inclusion of knowledge of Human Animal Relations in social work practice in Ontario and thinking about trans-species social justice.

2018· article· en· W2902433023 on OpenAlexaboutno aff
Jasmine Ferreira, Atsuko Matsuoka, John Sorenson

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSession (web analytics)Social justiceInclusion (mineral)Social workWork (physics)Human animalSocial psychologyPsychologySocial relationSociologySocial sciencePolitical scienceComputer scienceEcologyBiology
DOInot available

Abstract

fetched live from OpenAlex

Animals are increasingly being recognized as important family members and attachment figures in the lives of humans. Currently, over half of Canadian households include companion animals, making it very likely that social workers already work with individuals who have valued relationships with animals and consider them trans-species family members. Additional evidence of health benefits of animal-human interactions and relationships throughout the life-course warrants further consideration and more in-depth understanding of what social work practitioners know and do in relation to animal-human connections.\nHowever, studies conducted in the United States (Risley-Curtiss, 2010) and recent surveys in Nova Scotia, Saskatchewan, Manitoba and Alberta (Chalmers et al., 2015; Hanrahan, 2013) found significant gaps between social work practice and education around utilizing knowledge related to Human-Animal Bond/ Human-Animal Relations/Human-Animal Interactions. This paper reports on an online study conducted in Ontario, building on the above mentioned Canadian and US surveys, to explore what social workers in Ontario know and are doing around inclusion of animal-human relationships in their practice. A unique feature of the Ontario survey is its inclusion of a Critical Animal Studies perspective.\nTwo hundred and twenty-four registered social workers (RSWs) in Ontario who are currently employed in social work and related fields responded. Analysis of the survey data is underway. Preliminary results indicate that most social workers currently are not including animals or knowledge of Human Animal Relations in their practice or are unsure how to do so, as previous surveys found. Lack of understanding of how to include Human-Animal relations in practice has serious implications for social work. Importantly, lack of general awareness also suggests lower awareness of intersectional oppression of animals and humans and failure to address trans-species social justice (justice beyond humans). Preliminary results also indicate two thirds of the respondents have considered including animals in their practice but currently do not, due to various barriers and concerns such as workplace policies relating to risk to humans. The presentation will focus specifically on social workers’ qualitative responses provided and examine barriers to inclusion of knowledge of Human Animal Relations in their practice, using a Critical Animal Studies perspective to explore possibilities of moving toward trans-species social justice. The paper aims to contribute to better understanding of challenges social workers face in incorporating knowledge of Human Animal Relations in social work practice and policy development.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.349

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0170.003
Scholarly communication0.0030.002
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0250.003

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 head0.040
GPT teacher head0.324
Teacher spread0.284 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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