Bibliographic record
Abstract
Tracking the flow of outsourced work across borders and into the growing basin of precarious, non-unionized, and low-wage employment, this chapter looks at how the cybertariat is confronting communicative capitalism’s formidable powers of mobility. The chapter’s analysis of the relationship between the globalization of customer relations and the transnationalization of worker resistance opens with an overview of the trends shaping the transnational portion of the call centre industry, or what I refer to as global call centre capital . The “Calling for Change” campaign launched in 2008 by the upstart New Zealand union Unite in cooperation with the Australian National Union of Workers is a particularly compelling example of how capital flight can generate collective organization and conflicts in its wake. Crossing the Tasman Sea to pursue call centres outsourced from Australia, the campaign utilized a medley of tactics including brand tarnishing, picketing, wildcat, and even hunger strikes. The organizing arising at the other end of the outsourcing from Australia is especially significant, I argue, as its protagonists come from sectors of New Zealand’s workforce that are well outside those traditionally represented by the country’s labour movement, including women, teenagers, migrant workers, and indigenous populations. As such, the case not only offers insights into the feminization and racialization of the cybertariat, but also into its potential to animate a labour transnationalism that can produce a counter-force to the mobility of global capital’s most communicative sectors.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.032 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".