On Gothic romance and the happy ending: legislating the human rights of transnational migrant workers and their families
Bibliographic record
Abstract
We are on a tomato farm in Leamington, Ontario. Representatives of the Mexican consulate have been called to the farm to address some ‘trouble’ with Mexican workers employed on the farm. A supervisor, a young Canadian man in a T-shirt and with rumpled hair, explains, ‘last weekend we had a problem where four guys got really drunk. Um, they came back around four or five o’clock in the morning, ah, they caused a little bit of trouble here, did a little bit of damage’. One consulate representative, a youngish man in a neat button-down shirt, takes notes as he listens to the complaint. Another, an older man in a suit, nods intently. Both men are serious, and their behaviour is formal in front of the camera. The supervisor adds, grudgingly, ‘mnn, basically, I know, I know they’re human, and they’re gonna do this, but, like where do you . . . , where do you draw the line, you know?’ Through the camera lens, viewers (and evidently the consulate representatives) do not learn the specifics of the complaint – some workers got drunk, they caused a ‘little bit of trouble’, and they did ‘a little bit of damage’. The supervisor calls the workers in from the greenhouses to gather inside a warehouse so that the consulate representatives can speak to them. The workers are in jeans, shorts, and baseball caps. Their respective clothing marks a deep division between the two groups of Mexican nationals. The consulate representative explains, ‘[the boss] would like some moderation with alcohol’. A second representative, perhaps attempting to bridge the class division with the inclusive language of a national imaginary, adds: ‘Look, we’re all far from our country. Each one of us is an ambassador of the country. Because they will judge all of us. “How are the Mexicans?” “Well, I know one and he’s this and that” ’.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.041 | 0.042 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".