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Record W2030155312 · doi:10.1080/09540253.2014.927836

Re-imagining the (un)familiar: feminist pedagogy in rural spaces

2014· article· en· W2030155312 on OpenAlexaff
Anne Wagner

Bibliographic record

VenueGender and Education · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsNipissing University
Fundersnot available
KeywordsSociologyPerspective (graphical)Context (archaeology)Gender studiesPhenomenonPovertyIntersectionalityRural areaPedagogyFeminist pedagogyPlace-based educationFeminismPolitical scienceGeographyEpistemology

Abstract

fetched live from OpenAlex

Little attention has been directed towards post-secondary education in rural spaces, instead conceptualising university study primarily as an urban phenomenon. Challenging dominant conceptions of rural areas as backwards, conservative, poverty stricken and isolated, this article investigates the unique opportunities associated with rural universities. Exploring the impact of feminist pedagogy in a rural context from the perspective of critical scholars teaching in rural spaces, the analysis contends that these sites provide a unique position from which to begin tackling complex rural social issues. Adopting an intersectional approach is integral to assisting rural women to theorise the complexity of their lives, both locally and globally. Such teaching is especially significant for rural women, who otherwise may find academia inaccessible and disconnected from their lived reality.

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)0.006
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.046
Scholarly communication0.0070.009
Open science0.0010.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.019
GPT teacher head0.277
Teacher spread0.258 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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