Hidden sides of the credit economy: Emotions, outsourcing, and Indian call centers
Bibliographic record
Abstract
Engaging in credit is not necessarily a rational activity for consumers. Firms utilize sociological resources and techniques to convince them to sign on to credit accounts, maintain their purchasing habits, and pay on their debts. This analysis examines how global credit industries, their subcontracting firms, and their employees, carry this out. Focusing on Indian call centers, where Indian employees provide customer service for US consumers, this study reveals three hidden and inter-related dynamics of credit. First, emotions are integral to the credit industry. It relies on frontline labor, emotion workers who communicate directly with the consuming public. To secure credit, these employees learn and utilize strategies that appeal to customers’ intimacies (deep sensitivities and anxieties about money, family, self, etc.) and moralities (ethics about finance and sense of honor about paying debts). Second, credit is a driver of outsourcing. This industry has historically been the founder of, and continues to be, the primary client base for, offshore customer services in India. Third, outsourcing facilitates the emotional components of credit work. Moving these functions from the Global North to South enables credit firms to access highly skilled and inexpensive workers, and monitor their emotions rigorously in the ongoing labor process. This represents globalization of an affect economy, as Northern credit firms use outsourcing to extract emotional labor from the Global South (Hochschild, 2003). These firms face challenges, however, in translating US moralities and intimacies of credit to India, revealing a transnational cognitive dissonance in the meanings of credit and consumption.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".