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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".