‘In a Settled Country, Everyone Must Eat’: Four Questions About Transnational Private Regulation, Migration, and Migrant Work
Bibliographic record
Abstract
First, I would like to acknowledge where this paper was presented and where the work of revising it into an article took place. I would like to acknowledge the territory, which is not just Toronto, Ontario, Canada, but also Tkaronto, a Mohawk or Kaniekehaka word (as are Ontario and Canada). This word is from one of the languages of the Six Nations that comprise the Haudenosaunee Confederacy (People of the Longhouse), perhaps better known in this symposium by the French colonial name of Iroquois. Toronto and its surrounding territory are traditionally of the Huron-Wendat people, the Seneca Nation of the Haudenosaunee, and with title most recently lying with the Mississaugas of New Credit (Anishinabe). I would like to acknowledge the territory and thank these hosts, as well as the conference organizers for their generous invitation to participate in these discussions on transnational private regulation (TPR).
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.057 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".