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Record W2911842807 · doi:10.18357/ijcyfs101201918806

RESPONSES TO ADVERSITY FACED BY FARMING MEN: A GENDER-TRANSFORMATIVE ANALYSIS

2019· article· en· W2911842807 on OpenAlexafffundvenue
Philippe Roy, Émilie Duplessis-Brochu, Gilles Tremblay

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsUniversité LavalUniversité de MontréalUniversité du Québec à Chicoutimi
FundersCanadian Institutes of Health Research
KeywordsPsychologyTransformative learningSocial psychologyMasculinityConformitySolidarityFocus groupSocial supportQualitative researchCoping (psychology)SociologyDevelopmental psychologyClinical psychologyPolitical science

Abstract

fetched live from OpenAlex

The values that characterize the traditional and stereotypical image of rural masculinity put pressure on farming men to engage with risky behaviours, both physical and mental, and reduce their willingness to seek help. This paper investigates individual and social responses to adversity, under the lenses of response-based practice and gender-transformative health promotion. Our method is based on qualitative semi-structured interviews with 32 farming men and 2 focus group interviews with 14 experts on men’s health, farming, and rural social work. Results suggest gender is negotiated through individual and social responses to adversity, with fluid transitions between conformity and resistance with regard to traditional masculinity. Individual responses to adversity can include negative or positive coping strategies. Social responses can be supportive, or they can be marginalizing, such as the devaluation of farming. For farmers facing adversity, there is a disparity in social support, with communal solidarity being evident in a material crisis, but not in a personal one. Some community-based responses are highlighted for their ability to support farming men in coping with adversity.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.170

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 studies0.0000.000
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 head0.022
GPT teacher head0.253
Teacher spread0.231 · 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 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

Citations8
Published2019
Admission routes3
Has abstractyes

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