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Record W4392470498 · doi:10.14201/candb.v13i13-32

I Learned to Pick My Battles: Girls Dissenting in Oil Country

2024· article· en· W4392470498 on OpenAlexaff
Meighan Mantei

Bibliographic record

VenueCanada and Beyond A Journal of Canadian Literary and Cultural Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsCarleton University
FundersUniversidad de SalamancaWenner-Gren Foundation
KeywordsDissenting opinionPolitical scienceLaw

Abstract

fetched live from OpenAlex

In this paper I explore how girls living in a community economically reliant on the extraction of fossil fuels navigate gender expectations, loyalties, ideologies and moralities within their family structures, their places of employment and their affective communities. I describe how girlhood(s) within resource dependent communities are composed of and configured through the social, political, and economic conditions of extractivism, and the social relations that exist within these material conditions. The meeting of the material conditions of resource extraction and the social relations that exist within these environments, can be understood as “zones of entanglement.” An exploration of girls’ lives within these zones of entanglement, highlights how girls maneuver within the processes of social acceptance, belonging and notions of the “good life” by engaging in various strategies that work to create opportunities, while also reinforce foreclosures. These strategies include moving between speech and silence, learning to pick their battles, taking up space, and engaging in care-work. Through engagement in various strategies girls learn to protect themselves while maintaining opportunities for hope, connection, and transformation in their own lives, and in their interdependent relationships and attachments.

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)0.003
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.305

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0470.019
Scholarly communication0.0090.004
Open science0.0020.006
Research integrity0.0040.010
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 head0.024
GPT teacher head0.283
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

Citations0
Published2024
Admission routes1
Has abstractyes

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Same venueCanada and Beyond A Journal of Canadian Literary and Cultural StudiesSame topicMiddle East and Rwanda ConflictsFrench-language works237,207