Temporary labour migration, global redistribution, and democratic justice
Bibliographic record
Abstract
Calls to expand temporary work programmes come from two directions. First, as global justice advocates observe, every year thousands of poor migrants cross borders in search of better opportunities, often in the form of improved employment opportunities. As a result, international organizations now lobby in favour of expanding ‘guest-work’ opportunities, that is, opportunities for citizens of poorer countries to migrate temporarily to wealthier countries to fill labour shortages. Second, temporary work programmes permit domestic governments to respond to two internal, contradictory political pressures: (1) to fill labour shortages and (2) to do so without increasing rates of permanent migration. Temporary work programmes permit governments to appear ‘tough’ on migration, while responding to employer pressure to locate workers willing to work in low-skilled, poorly remunerated positions. The coincidence of national self-interest and global justice generates a strong case in favour of expanding guest-work. We evaluate the moral benefits and burdens of expanding guest-work opportunities, and conclude that although there are benefits to be gleaned from the perspective of global wealth redistribution, at present, temporary work programmes are generally unjust. We will argue that just temporary work programmes, in time, permit temporary workers to attain citizenship. This spells the end of traditional temporary work programmes, which require that workers return to their home country in time; instead, what is temporary is the employment obligation that must be fulfilled as a requirement to access citizenship. As long as this requirement is met, we endorse guest-work programmes as a tool to respond to global inequality.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".