Labour market regulation as global social policy: The case of nursing labour markets in Oman
Bibliographic record
Abstract
This article examines global social policy formation in the area of skilled migration, with a focus on the Gulf Arab region. Across the globe, migration governance presents challenges to multiple levels of authority; its complexity crosses many scales and involves a multitude of actors with diverse interests. Despite this jurisdictional complexity, migration remains one of the most staunchly defended realms of sovereign policy control. Building on global social policy literature, this article examines how 'domestic' labour migration policies reflect the entanglement of multiple states' and agencies' interests. Such entanglements result in what we characterize as a 'multiplex system', where skilled-migration policies are formed within, and shaped by, globalized policy spaces. To illustrate, we examine policies that shape the nursing labour market in Oman during a period when the state aims to transition from dependence on an expatriate to an increasingly nationalized labour force. Engaging a case-study methodology including a survey of migrant healthcare workers, semi-structured interviews and data analysis, we find that nursing labour markets in Oman represent an example of global policy formation due to the interaction of domestic and expatriate labour policies and provisioning systems. The transnational structuring of policy making that emerges reflects a contingent process marked by conflicting outcomes. We contend that Oman's nursing labour market is an example of new spaces where global social policies emerge from the tension of competing national state and market interests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".