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Record W4401959568 · doi:10.1108/jap-05-2024-0027

“Racialized persons have a completely different experience”: the experiences of older adults with the Toronto Police Service

2024· article· en· W4401959568 on OpenAlexaffabout
Kristina M. Kokorelias, Anna Grosse, Dara Dillion, Joshua Wyman, Elsa Nana Nzepa, Meena Bhardwaj, Andrea Austen, Samir K. Sinha

Bibliographic record

VenueThe Journal of Adult Protection · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsWestern UniversityThe King's UniversityOntario Long Term Care AssociationSinai Health SystemToronto Rehabilitation Institute
Fundersnot available
KeywordsService (business)PsychologyGerontologyMedicineBusinessMarketing

Abstract

fetched live from OpenAlex

Purpose This paper aims to provide an in-depth examination of culturally and linguistically diverse older adults’ perceptions of and experiences with the Toronto Police Service to inform the development of a training curriculum for police officers working with older persons. Design/methodology/approach A qualitative descriptive study using virtual focus groups with 26 older adults from Toronto was conducted. Findings Three main themes emerged: understanding intersectionality; the impact of police officer attitudes and biases; and the need for age-friendly policing. Although many older adults rely on police services to keep them safe, there is dissatisfaction with some aspects of how police interact with older adults, particularly from minority groups. Originality/value Participants were older adults from culturally, ethnically and linguistically diverse backgrounds who are not usually included in studies on improving police services.

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)0.005
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 score0.808
Threshold uncertainty score0.382

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.006
Scholarly communication0.0030.003
Open science0.0010.007
Research integrity0.0010.002
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 head0.041
GPT teacher head0.346
Teacher spread0.305 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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