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Record W2923408056 · doi:10.1177/0886260519836782

Distinct Places to Address Intimate Partner Violence

2019· article· en· W2923408056 on OpenAlexafffund
Anthony Piscitelli, Sean Doherty, Stephanie Francis

Bibliographic record

VenueJournal of Interpersonal Violence · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsWilfrid Laurier UniversityConestoga College
FundersRoyal Canadian Geographical Society
KeywordsDomestic violencePlacemakingHarmGrounded theoryQualitative researchPoison controlSociologyService (business)Service providerMeaning (existential)Suicide preventionPublic relationsCriminologySocial psychologyPsychologyMedicinePolitical scienceEngineeringUrban planningBusinessSocial scienceMedical emergency

Abstract

fetched live from OpenAlex

The concept of place can be used to address intimate partner violence (IPV). Place, to geographers, is a concept that helps explain how human experiences shape a sense of meaning surrounding locations. Using a grounded theory approach and qualitative interviews with service providers, we present a case study exploring how Brantford social service agencies apply placemaking strategies and take advantage of the elements of place to reduce the harm associated with IPV. Six themes arose in the interviews. Home, the women's shelter, courts, and schools were found to represent unique areas where placemaking strategies help to reduce harm. Hair salons emerged as a unique place to reach victims, whereas prison was a place that encouraged offenders to make changes. These themes show the concept of place has the potential to add insights into how IPV can be reduced and the traumas facing victims addressed.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.329
Teacher spread0.309 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2019
Admission routes2
Has abstractyes

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