Landscapes of Rural Girlhood: Navigating Belonging in Southeast Saskatchewan
Bibliographic record
Abstract
This dissertation examines the ways gender, class, race and age intersect with the industrial complex of rural southeast Saskatchewan to shape rural girls’ embodied experiences of girlhood by engaging with concepts of affect, relationality, and a politics of belonging. By exploring rural life through the lived experiences of women and girls, this research project explores the structural forms of inequality women and girls encounter within industrial capitalism and extraction, colonialism, racism, and heteropatriarchy, and the ways in which they navigate and challenge these forms of inequality in their everyday lives. The methods and approaches used for this study include participant observation, open-ended and semi-structured interviews both in-person and virtually, autoethnography, and photovoice. Fieldwork for this project took place over 16 months in 12 rural communities heavily entangled in the fossil fuel and agricultural industries in southeast Saskatchewan. In exploring the everydayness of the politics of belonging, and following Sara Ahmed’s notion of happiness scripts, I identify three gendered scripts that impact rural girls’ experiences and feelings of belonging and connection within their affective communities. I name these as follows: they become wives, country girl power, and tough femininity. These gendered scripts are imposed on, expected of, and taken up by, girls in the creation of belonging and connection to their affective communities, and in their creation of ‘home’ within the landscapes of their rural girlhood(s). The work of maneuvering through these gendered scripts of rural belonging requires that girls learn to ‘pick their battles’ and engage navigation strategies that maintain their safety and interdependent relationships, while at the same time, providing opportunities for self-actualization and self-determination. With the intention of nudging girls away from the margins and into spaces of visibility within the landscape of rurality and industrialization, this dissertation maps some of the moments in which girls become disconnected, invisible and silenced, and other moments when they appear, connect and create opportunities to live self-determined lives. By making girls subjects of their own stories, this research illuminates the ways girls share information and ideas about the people, places and things that hold meaning for them as rural girls.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".