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Record W1970024024 · doi:10.1080/0966369x.2011.572432

Farmwomen's emotional geographies of care: a view from rural Ontario

2011· article· en· W1970024024 on OpenAlexafffundabout
Rachel Herron, Mark W. Skinner

Bibliographic record

VenueGender Place & Culture · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsTrent University
FundersRoyal Canadian Geographical SocietyRoyal Geographical SocietyTrent University
KeywordsSituatedSociologyNegotiationObligationPower (physics)Resistance (ecology)Health careEmbodied cognitionFocus groupWork (physics)Emotional laborEmotion workGender studiesPublic relationsEconomic growthPsychologyPolitical scienceSocial psychologySocial science

Abstract

fetched live from OpenAlex

This article contributes to geographies of rural women's health by investigating farmwomen's perceptions of their caring roles and responsibilities, which are crucial to the wellbeing and sustainability of rural people and their communities. Featuring a thematic analysis of interviews and a focus group with farmwomen from Ontario, Canada, the research examines farms and farming as unique places and spaces of care. Informed by the literature on emotional geographies, the article examines how care is situated and performed through farmwomen's negotiation of multiple, overlapping identities and how these are embodied and affective in emotional work. The findings not only confirm the paramount role of women in rural care, they demonstrate the interdependence of family, community and work as central to the challenges of rural women's health. The article argues that the link between health and productivity on the farm is crucial to understanding farmwomen's caring, and highlights the paradox that their emotional work is as much about opportunities for power and resistance as it is about obligation and subordination.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.050
Threshold uncertainty score0.363

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0270.009
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.194
Teacher spread0.174 · 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

Citations44
Published2011
Admission routes3
Has abstractyes

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