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Record W144456741

Have You Heard the One About the Farmer's Daughter? Gender Bias in the Intergenerational Transfer of Farm Land on the Canadian Prairies

2005· article· en· W144456741 on OpenAlexvenueaboutno aff
Ella Forbes-Chilibeck

Bibliographic record

VenueCanadian women's studies · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsDaughterJokeLivelihoodHerdingSociologyAsset (computer security)HistoryLawPolitical scienceAgricultureLiteratureArt
DOInot available

Abstract

fetched live from OpenAlex

In Canadian rural culture, the joke has been around forever.' Predictable and formulaic in nature: these jokes are not sophisticated-the plot involves the seduction of the daughter by the stranger1 traveling salesman while the humour is interjected when it turns out that the father, and perhaps the daughter herself, has manipulated the stranger into ma~riage.~ The father is seen to be successful in turning the tables on the young man. Why would anyone find this funny? Looking at the nature ofjokes; the act of tellinga joke tests ifthe audience displays the shared knowledge or social values imparted by the joke.4 The farmer's daughter joke would not be funny if one did not share certain perceptions of the farmer and his daughter. Humour is found in the incongruity of the expected result and the punch line in the joke5-we laugh at the unexpected. The underlying subtext is, however, that the daughter must rely on her cunning, physical attractiveness, and manipulative father to trap a husband and thereby secure her economic future. All of these jokes are premised on the idea that there is a need to marry off the daughter so she is no longer a burden on the farm's economic viability. Jokes such as these are a manifestation of the powerlessness of the farmer's daughter in much of Canadian rural culture. O n the prairies,6 farmland is often one of the few means by which to earn a livelihood as well as the largest family-held asset. Farm property is traditionally passed from father to son, often leaving a farmer's daughter with few resources. In Radchenko v. Radchenko the court acknowledged that if farmers' daughters brought any assets at all into the marriage, they were usually few and tended to be consumable when it recognized that this story is a typical one: the wife brought to the marriage a cow and a heifer.' This pre-marital economic disparity has farreaching implications for farm women. In the rural, male-dominated and often traditional environment, the farmer's daughter is particularly vulnerable. It is this pervasive lack of opportunity that one acknowledges, subconsciously or not, in finding humour in the farmer's daughter joke. This articlewill explore how traditional property rights, gender-specific property transferlinheritance practices, and a patriarchal culture have come together on the Canadian prairies to create a systemic gendered disempowerment of women in general and farmers' daughters in par t ic~lar .~ Acquiring property rights is a particularly important human rights issue9 because without property rights, individuals are unable toeffectively exercise other human rights.'' Inequality in land ownership is a major hindrance in correcting internationally recognized problems women face such as generally inferior economic status. Typically, Canada is not perceived as a country where such infringements exist. There are no formal legal restrictions to women acquiring property in Canada, in part due to legal and political recognition in the first few decades of the twentieth century and subsequent legislative changes.' Lawreform, however, does not operate in a vacuum independent of the various social relations, and patriarchy is one such set of relations.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.249

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.070
GPT teacher head0.250
Teacher spread0.180 · 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 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

Citations26
Published2005
Admission routes2
Has abstractyes

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