Post crisis conditions of work and employment in Indian BPO
Bibliographic record
Abstract
In the context of the globalization of business services from 2000, most attention focused on the high-profile offshoring of call centres from the developed economies of the global North (United State, United Kingdom, Canada) to the so-called developing economies of the global South, particularly India (e.g. Dossani and Kenney, 2007). Contrasting but complementary challenges confronted organised and organising labour at both nodes of capital’s transnational servicing chains (Taylor and Bain, 2008). However, these important debates rested exclusively on evidence derived from the period preceding the crisis of 2008. A re-evaluation is now required based on the re-configured political economy of Business Process Outsourcing (BPO) and changed conditions of work and experiences of labour in the offshored industry. Given the lacuna of published work on post-crisis Indian BPO, this paper cuts new ground. It examines the dynamics of work and employment across capital’s three contrasting servicing chain relationships (Indian third-party, global third-party provider, in-house), that span the spectrum of offshoring. Evidence from in-depth interviews with senior managers, middle managers and, crucially, agents engaged on ‘voice’ and back-office services indicate somewhat differing conditions and experiences that emerging labour organising strategies need to acknowledge.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".