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Record W3177583306 · doi:10.1111/irel.12286

Collective Voice and Worker Well‐being: Union Influence on Performance Monitoring and Emotional Exhaustion in Call Centers

2021· article· en· W3177583306 on OpenAlexaff
Sean O’Brady, Virginia Doellgast

Bibliographic record

VenueIndustrial Relations A Journal of Economy and Society · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCollective bargainingEmotional exhaustionEmployee voiceEmotional laborTrade unionFocus groupPolitical sciencePsychologyPublic relationsNon unionSocial psychologyBusinessLabour economicsEconomicsBurnoutMarketingClinical psychologyMedicine

Abstract

fetched live from OpenAlex

This study analyses the impact of union actions on worker well‐being. It examines union efforts to reduce emotional exhaustion associated with performance monitoring in call center jobs, drawing on quantitative ( N = 1894) and qualitative (interviews and focus groups) data in the United States. Findings suggest that the effective exercise of collective voice, through collective bargaining and activism by union officials, can reduce emotional exhaustion by promoting fair and developmental monitoring practices.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.027
GPT teacher head0.268
Teacher spread0.242 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations26
Published2021
Admission routes1
Has abstractyes

Explore more

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