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Duelling Discourses at Work: Upsetting the Gender Order

2017· book-chapter· en· W1555660438 on OpenAlexaff
Kelly Dye, Albert J. Mills

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsAcadia University
Fundersnot available
KeywordsSilenceGender studiesPrivilege (computing)SociologyReification (Marxism)Order (exchange)Critical discourse analysisQueerHeteroglossiaNarrativeEpistemologyPolitical scienceAestheticsLinguisticsPoliticsLaw

Abstract

fetched live from OpenAlex

Abstract Findings of an extensive archival study of Pan American Airways (PAA) strongly support Acker’s (1990) notion of the presence and importance of a dominant discourse of organizing logic in structuring a gendered order. Findings also demonstrate that the presence of alternative, but not necessarily feminist, discourses can serve to upset the gender order of organizations. Thus, we conclude that changing the organization’s gender substructure (Acker, 1992b) by changing the dominant discourse or introducing competing discourses may help to destabilize “truths” and interrupt the perpetuation and reification of policies, practices, and understandings that are often taken-for-granted despite their ability to silence voices and privilege some groups over others.

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)0.010
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0150.038
Scholarly communication0.0140.013
Open science0.0020.013
Research integrity0.0020.005
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 head0.226
GPT teacher head0.331
Teacher spread0.106 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2017
Admission routes1
Has abstractyes

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