Bibliographic record
Abstract
An exploration of the link between pacification and global apartheid in the context of the racialized effects of neoliberal labour migration is undertaken. Drawing on the general layout of Canada’s temporary labour migration regime, the legal regulation of migrant labour is taken as a project of pacification that enforces apartheid conditions. Juxtaposed against the construction of migrant labour as menace or threat to ‘host’ communities in Canada, the growing need for “armies of offshore labour” presents an especially acute challenge for capital and national states. Despite certain perceptions that it is freed from national state constraints owing to the hyper-competitiveness of contemporary migration, capital remains deeply beholden to the politico-legal interventions of states, both sending and receiving. Situated within the hierarchical and uneven logic of the nation-state system and global capitalist development, pacification becomes a way in which capital and states attempt to mediate contradictions and govern not “insecurities” surrounding human mobility but rather the need to fabricate productive labour, a need contingent upon the complex transnational legal regulatory dynamic of unfree migrant labour which itself relies upon and perpetuates apartheid.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".