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Record W3158187074 · doi:10.1177/0959353521989526

“Small town girls” and “country girls”: Examining the plurality of feminine rural subjectivity

2021· article· en· W3158187074 on OpenAlexaffabout
Sara Crann

Bibliographic record

VenueFeminism & Psychology · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSubjectivityGender studiesGirlEmpowermentNarrativeSociologyRural areaPsychologyDevelopmental psychologyPolitical science

Abstract

fetched live from OpenAlex

Despite growing scholarly interest in the identities and experiences of girls, little attention has been paid to the identities and experiences of rural girls, and in particular how girls’ subjectivities are discursively constituted in rural spaces. Using interviews and focus group discussions with girls and young women who attended a girls’ empowerment program, this paper draws on feminist poststructuralism and positioning theory to examine how rural gendered subjectivities are constructed and negotiated by girls and young women within the social, spatial, and discursive boundaries of a rural Canadian community. I examine how the girls and young women positioned themselves and were positioned by others as “small town girl” and “country girl” subjects, and how rural positionality was accomplished through invoking real and imagined notions of more urban “others.” It is through these contrasts to urban subjecthood that the variability of rural positionality is made visible. The findings of this study complicate and extend the dominant narrative of the urban-rural binary, and gendered identities and performances within rural spaces, by demonstrating the plurality of feminine rural subjectivity. This study offers new applications for the role of girls’ empowerment programs in shaping girls’ identities, experiences, and perspectives.

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)0.001
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 score0.215
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.035
GPT teacher head0.263
Teacher spread0.229 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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