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Record W2123677140 · doi:10.1111/anti.12147

The Family Behind the Farm: Race and the Affective Geographies of Manitoba Pork Production

2015· article· en· W2123677140 on OpenAlexaffabout
Kate Cairns, Deborah McPhail, Claudyne Chevrier, Jill Bucklaschuk

Bibliographic record

VenueAntipode · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsScrutinyPrideContext (archaeology)White (mutation)Race (biology)Production (economics)Family farmSpace (punctuation)SociologyAffect (linguistics)Gender studiesPolitical scienceGeographyLawAgricultureEconomics

Abstract

fetched live from OpenAlex

As increasing toxicity of Manitoba lakes garners public concern, the environmental impacts of pork producers have come under scrutiny. In this context, the Manitoba Pork Council launched The Family Behind the Farm, a series of advertisements and testimonials featuring pork producers and their families. We examine how this campaign operates affectively to distance the family farm from industrial pork production. Building upon geographical literature theorizing the relationship between race and affect, we argue that the campaign mobilizes pride in the family farm through heteronormative and racialized affects of intimacy, tradition, and intergenerational continuity. In the process, not only is pork production made innocent, but the family farm, and rural Manitoba itself, is reproduced as a white, heteronormative space with an innocent past and secure future. By analyzing this specific case, the paper demonstrates the role of the heteronormative family in reproducing affective geographies of whiteness.

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)0.001
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 score0.810
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.027
GPT teacher head0.300
Teacher spread0.273 · 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

Citations21
Published2015
Admission routes2
Has abstractyes

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