Labour Market Integration as an Interactive Process
Bibliographic record
Abstract
An undocumented female immigrant in New York has no medical insurance to allow the doctor to visit, shares a shabby apartment with a few other marginalised immigrants, has very limited money for groceries, and jumps between various gig jobs. An iconic case of seemingly failed labour market integration, this woman somehow manages to survive through the support from her migrant solidarity network. A local ethnic shop owner gives her free groceries, while an immigrant taxi driver offers her free rides from job to job. Out of the blue, a stranger hires her for a one-night job in an underground casino, without, however, clarifying her prospective duties. This is how Luciana, the protagonist in the movie Most Beautiful Island, engages in a high-risk informal market game of touching venomous insects to entertain rich clients. The final scene shows Luciana as the winner and sole game survivor, who leaves catatonically but with a tangible cash boon in her purse. The parting smile she then gives us is, nevertheless, telling in that she is determined to come back to play again. In fact, this gaming experience has changed her life forever. She has eventually found a way to earn a lot of money in a short time and resolve all her economic problems while also having proved her skill for this difficult job.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".