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Record W4308449813 · doi:10.1111/gwao.12910

The Chihuahua and the Space Princess writing in the margins: Antenarratives of two (older) women early career academics

2022· article· en· W4308449813 on OpenAlexaff
Adriana van Hilten, Stefanie Ruel

Bibliographic record

VenueGender Work and Organization · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsAthabasca UniversityToronto Metropolitan University
Fundersnot available
KeywordsScholarshipIntersectionalityNegotiationGender studiesResistance (ecology)SociologySilenceSpace (punctuation)Embodied cognitionTransition (genetics)TokenismPedagogyPsychologyAestheticsPolitical scienceSocial scienceArtLawEpistemology

Abstract

fetched live from OpenAlex

Abstract In asking “why is it so bloody hard for older women, with years of professional experience in industry and graduate degree holders, to create a career in the Academy?,” the authors co‐examine the challenges they experience in their careers and in their transition into academia. Framed within intersectionality scholarship, specifically anchor points, the authors' nonnormative positioning as older ciswomen, early career academics, who are attempting to find tenured positions, showcase their fluid living stories of sexism and ageism while trying to negotiate a career transition. Using a duoethnographic methodology, the authors share and then analyze their embodied, fragile antenarratives as they understand them at this point in time. In the process of writing differently, literally at the left and right margins of content and style, and in the analysis of these writings, they discover that they can begin to resist the gendered ageism from the margins and that they are no longer alone. They navigate a path of resistance, laboring against the norms that try to relegate two older women, early career academics' stories to silence, inviting others into their living story network.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.047
GPT teacher head0.259
Teacher spread0.211 · 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.

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

Citations10
Published2022
Admission routes1
Has abstractyes

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