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Record W2582551396 · doi:10.1080/2373566x.2016.1278178

Crossing Oceans: Testimonial Theatre, Filipina Migrant Labor, Empathy, and Engagement

2017· article· en· W2582551396 on OpenAlexafffundabout
Geraldine Pratt, Caleb Johnston

Bibliographic record

VenueGeoHumanities · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsUniversity of British Columbia
FundersEconomic and Social Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsTestimonialEmpathyPsychologySociologySocial psychologyAdvertisingBusiness

Abstract

fetched live from OpenAlex

Framed within the geopolitics of empathy, we describe the process of taking a testimonial play, based on verbatim research interview transcripts with Filipina domestic workers, their families, employers, and nanny agents in Vancouver, Canada, to the Philippines to present to audiences there, including to members of domestic workers’ families. Aspects of the script and the staging of the play were reworked to solicit empathy in a context where the challenging experiences of domestic workers in Canada sometimes are not heard—even within families, given the popular image of Canada as a land of opportunity and the perception that Canada is relatively inclusive in terms of labor rights and paths to permanent residency. Interviews with seven family members who attended the play offer some clues to assessing performance as a space for critical, sometimes uncomfortable forms of empathy. Rather than a purely affirming experience, we argue that empathy can lead to a fuller recognition of the suffering on which one’s good life depends and challenge fantasies of the good life, in this case, attainable through migration to Canada.

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)0.004
metaresearch head score (Gemma)0.011
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 score0.023
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0230.011
Scholarly communication0.0060.003
Open science0.0010.009
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.058
GPT teacher head0.331
Teacher spread0.273 · 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

Citations17
Published2017
Admission routes3
Has abstractyes

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