Expanding the Movement of Natural Persons Through Free Trade Agreements? A Review of CETA, TPP and ChAFTA
Bibliographic record
Abstract
Researchers and international institutions have tried to solve a fundamental paradox in the politics of migration. While introducing stricter migration policy stands high on the agenda of many countries, demographic facts suggest that they will need to introduce more extensive labour immigration to avoid labour shortages. Meanwhile, attempts to introduce a legally binding international regime on labour mobility, most ambitiously through Mode 4 of the General Agreement on Trade in Services (GATS) and as requested by developing countries, have had limited success. This article explores one of the political options for resolving this: regulating the movement of natural persons through free trade agreements. It examines three recently concluded free trade agreements (FTAs), the EU–Canada Comprehensive Economic and Trade Agreement (CETA), the China–Australia Free Trade Agreement (ChAFTA) and the Trans-Pacific Partnership (TPP), in an attempt to answer two questions. First, do the signatories commit to more expansive possibilities for labour mobility than through the GATS? Second, what has the political reception of such measures been? While most of the signatories are willing to schedule more far-reaching commitments through FTAs than through the GATS, these commitments typically fall within the realm of existing work permit systems in domestic law. In addition, we find examples of political backlash in countries that have included somewhat more ambitious mobility provisions in FTAs, particularly in Australia. These FTAs may still play a role by improving mutual recognition of skills, and limiting the impact of national reforms to restrict labour migration. However, we conclude that FTAs appear to be neither a manifestly successful instrument for significantly liberalizing labour mobility, nor an evidently desirable one. We call for a more holistic approach that refrains from temporary labour mobility programmes to meet permanent demand for labour, with respect for migrant workers’ rights at its core.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".