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Record W1978043777 · doi:10.1177/1077800411431555

An Ethnodrama on Work-Related Learning in Precarious Jobs

2012· article· en· W1978043777 on OpenAlexaffabout
Jasjit Kaur Sangha, Bonnie Slade, Kiran Mirchandani, Srabani Maitra, Hongxia Shan

Bibliographic record

VenueQualitative Inquiry · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of CalgaryYork UniversityUniversity of Toronto
Fundersnot available
KeywordsResistance (ecology)SociologyWork (physics)Embodied cognitionStereotype (UML)Ethnic groupRace (biology)Gender studiesPublic relationsSocial psychologyPsychologyPolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

This article is based on a research project on the lived experiences of precarious workers in Toronto, Canada. Using interviews with women in part-time, contract, and temporary jobs in three sectors (telemarketing, retail, and garment), the project explores the ways in which racial hierarchies structure jobs as well as forms of resistance that women exercise at work. The authors find that racialized processes stereotype workers and their skill sets, organize their work, determine their access to and exclusion from certain types of jobs, and impose cultural rules that classify and essentialize them in terms of race, language, and ethnicity. In this article, the authors use ethnodrama to represent their findings from this research project. Ethnodrama is a form that is well suited for this work because it allows us to bring the data to life through an embodied performance.

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)0.004
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 score0.155
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0240.027
Scholarly communication0.0050.003
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.179
GPT teacher head0.526
Teacher spread0.346 · 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

Citations18
Published2012
Admission routes2
Has abstractyes

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