Bibliographic record
Abstract
Call centers have come, in the last three decades, to define the interaction between corporations, governments, and other institutions and their respective customers, citizens, and members. The offshoring and outsourcing of call center employment, part of the larger information technology and information-technology-enabled services sectors, continues to be a growing practice amongst governments and corporations in their attempts at controlling costs and providing new services. While incredible advances in technology have permitted the use of distant and "offshore" labor forces, the grander reshaping of an international political economy of communications has allowed for the acceleration of these processes. New and established labor unions have responded to these changes in the global regimes of work by seeking to organize call center workers. These efforts have been assisted by a range of forces, not least of which is the condition of work itself, but also attempts by global union federations to build a bridge between international unionism and local organizing campaigns in the Global South and Global North. Through an examination of trade union interventions in the call center industries located in Canada and India, this book contributes to research on post-industrial employment by using political economy as a juncture between development studies, the sociology of work, and labor studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".