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Record W3167947420 · doi:10.1177/10608265211018817

A Men’s Survey: Exploring Well-Being, Healthy Relationships and Violence Prevention

2021· article· en· W3167947420 on OpenAlex
Liza Lorenzetti, Vic Lantion, David Este, Percy Murwisi, Jeff Halvorsen, Tatiana Oshchepkova, Hemlata Sadhwani, Fanny Oliphant, Adrian Wolfleg, Michael A. Hoyt

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Journal of Men s Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsAlberta HealthNetwork for Business SustainabilityUniversity of Calgary
FundersCalgary Foundation
KeywordsIndigenousPsychologyPopulationDomestic violenceSuicide preventionGerontologyGender studiesPoison controlSociologyMedicineDemographyEnvironmental health

Abstract

fetched live from OpenAlex

The participation of men is critical to preventing domestic violence, however, there is still little understanding of the capacities and supports that men need for well-being and healthy relationships. A men’s survey was designed to explore and identify the capacities and resources required by a diverse population of Canadian men. Data was collected on-line and through trained community-based research assistants. Over 2,000 men from 20 ethno-cultural groups responded, and multiple challenges and enablers were identified. Responses from Indigenous and African Canadian men highlight the need for an intersectional lens in understanding men’s well-being and violence prevention.

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.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.122
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.194
GPT teacher head0.399
Teacher spread0.205 · 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