Labor here, consume there, accumulate everywhere
Bibliographic record
Abstract
Probing beyond statistics, Barber and Bryan’s chapter examines the improvisations of capital and labor along the Philippines–Canada migration corridor. With reference to ethnographic cases coinciding with the feminization of Philippine labor export in the 1980s and the restructuring of Canada’s immigration system since 2008, the chapter explores the experiences over time of many well-qualified migrants arriving in Canada as temporary workers traveling under different visa programs where increasingly well-educated women predominate. Many well-educated Filipinos drawn to Canadian futures have sought transfer to permanency through targeted federal and/or provincial immigration programs described here. The authors argue that the social reproduction of Philippine migration can be linked to women’s social reproductive labor in the care sector and later in healthcare and food services, where migrant contributions to mobile capital as workers in Canada and consumers in the Philippines, in one global food service corporation, well illustrates the social reproduction of Philippine migration with its anchoring in feminized migration. The chapter concludes with a brief mention of how the COVID pandemic exposed the continuing vulnerability of Philippine migrant workers despite their major contributions to the nation’s economy and to the countries where their labor services the accumulation projects of internationally mobile capital.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".