Gendered and generational experiences of place and power in the rural Irish landscape
Bibliographic record
Abstract
The out-migration of young people from rural regions is a selective and highly gendered process suggesting considerable differentiation in the way young men and women identify with and experience rural life. Gender imbalance in rural youth out-migration has prompted feminist researchers to consider more carefully linkages between the gendered nature of rural space and place and the social and spatial mobility of rural young men and women. Based on 11 months of ethnographic fieldwork in a rural Irish fishing community, this article explores the gendered dimensions of rural youth experience. Theoretically grounded in the conceptual triad of gender, power and place, this article considers how young men and women experience ‘the rural’ as masculine and feminine subjects. Special attention is given to the ways in which relations of power in ‘the rural’ are articulated, contested and accommodated in the everyday lives of local young men and women. As well as highlighting the ways in which rural space and place is male-dominated, this article foregrounds other power relations at play in the rural. As part of this effort, I problematize male power and point to the ‘effectivity of girls as conduits of power’. I argue that subjectivities of intra-gender relations are a critical dimension of rural youth experience and cannot be overlooked in research on rural youth experience and emigration.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".