Flesh in voice: The no-touch embodiment of transnational customer service workers
Bibliographic record
Abstract
Telephone-based customer service work is often conceptualized as disembodied. Automatic dialing systems direct callers through menu-driven options, and eventually to a distant customer service worker. Interactions are scripted, and workers have little job discretion to deal with out-of-the-box customer requests. Yet, although the bodies of call center workers and their customers do not come into contact, this article considers whether their interactions are in fact disembodied. Based on interviews with transnational customer service workers in India, I argue that bodies matter in remote customer service interactions. Part of the job of a customer service worker is the transmission of bodies through voice. This involves making sense of how ideal workers are embodied in callers’ eyes and using their voices to emulate these imagined ideal workers. I argue that exploring the embodiment of ‘voice workers’ extends analyses of embodiment to date, which have focused primarily on whole bodies in physical contact with others. The findings presented here highlight the importance of interpellation—specifically the work of ‘reading bodies’ which is a significant part of service work, especially work which crosses national borders. Bodies are ‘read’ based on social and historical contexts within which people are immersed and these contexts are influenced by social stratification, state policies, and colonial histories.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".