Land, Resources, and a Politic of Affect: Navigating Girlhood in Oil Country (WGF - Dissertation Fieldwork Grant)
Bibliographic record
Abstract
This resource is an application for a Dissertation Fieldwork Grant from the Wenner-Gren Foundation. Set against a backdrop of ‘I love oil and gas’ bumper stickers, Texas and Roughrider flags, pro-Trump and antivaccine rhetoric, and American style populist politics, in this study I explore the ways in which girlhood is constituted through embodied markers and affective constellations of (un)belonging and (dis)connection in relation to the local fossil fuel and agricultural industries of southeast Saskatchewan. I approach this research acknowledging that girls’ lives do not emerge in a vacuum, but from the interactional context in which they are deeply entangled including their family stories, their social landscapes, and their relationships of trust. By engaging with intersectional approaches, I explore the various racialized, classed, gendered, and sexualized experiences that constitute local notions of ‘girl’ and ‘girlhood’ and the ways in which girls engage with these affective and embodied experiences to navigate, reify, and challenge normative conceptions of girlhood. With attention to a politics of affect, I seek to illustrate the situated and ethical orientations of the specificities of rural Saskatchewan and the possibilities for attending to girls’ various responses towards the complex and messy worlds in which they live and which they are inheriting.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".