‘There will be no law, or people to protect us’: Irregular Southeast Asian seasonal workers in Taiwan before and during the pandemic
Bibliographic record
Abstract
Abstract This paper investigates the everyday lived realities of Southeast Asian migrant workers who left the formal sector of the labour market and entered the informal agricultural sector before and during the COVID‐19 pandemic in Taiwan. Drawing on observations of migrants' daily lives and farm work and 19 in‐depth interviews, it delves into migrants' subjective experiences of vulnerability, paternalism, exploitation, and control at work due to a lack of legal protection and the illegality of their employment. Although the literature has identified a link between ‘running away’ from formal employment and seeking freedom, this research suggests a continuum between experiences of work in the formal and informal economic sectors. The paper sheds new light on mobility, work, illegality, and informality and how these have constantly shaped ‘runaway’ workers' subjective experiences of freedom and unfreedom during the pandemic.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".