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Record W7125373479 · doi:10.48512/xcv8501906

Land, Resources, and a Politic of Affect: Navigating Girlhood in Oil Country (WGF - Dissertation Fieldwork Grant)

2022· article· en· W7125373479 on OpenAlexaboutno aff
Meighan Mantei

Bibliographic record

VenueThe Digital Archeological Record (tDAR) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsnot available
Fundersnot available
KeywordsSituatedEmbodied cognitionContext (archaeology)PoliticsNormativeRelation (database)Style (visual arts)

Abstract

fetched live from OpenAlex

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 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)0.001
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 score0.401
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.022
GPT teacher head0.299
Teacher spread0.277 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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