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Record W2905402124 · doi:10.1111/ntwe.12125

‘Get paid, get out’: online resistance to call centre labour in Canada

2018· article· en· W2905402124 on OpenAlexaffabout
Matthew S. Johnston, Genevieve Johnston, Matthew D. Sanscartier, Mark Ramsay

Bibliographic record

VenueNew Technology Work and Employment · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsCarleton University
Fundersnot available
KeywordsEmotiveResistance (ecology)AlienationSpace (punctuation)Public relationsWorkforceSociologyPolitical scienceMedia studiesLawComputer science

Abstract

fetched live from OpenAlex

This qualitative content analysis of 503 anonymous online reviews of 52 Canadian call centres posted on RateMyEmployer.ca explores how forms of resistance, alienation and emotional labour are expressed outside of the workplace. Our study finds that digital publics are producing emotive insurgencies and networks of support within marginalised communities that undermine employers’ attempts at deadening the workforce. The reviews exemplify worker awareness of exploitation as some connect these issues to broader socio‐economic factors that are beyond their control. While many offer tactics to challenge and destabilise their working conditions and culture as well as heartfelt and sarcastic warnings of what one might expect if they pursue call centre employment, others use the online space as a means of venting frustrations, eliciting empathies and expressing sentiments of hope(lessness).

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)0.038
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 score0.134
Threshold uncertainty score0.474

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0230.012
Scholarly communication0.0100.002
Open science0.0030.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.298
Teacher spread0.280 · 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

Citations12
Published2018
Admission routes2
Has abstractyes

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