Ireland’s immigration policies (1997–present): Links to global trends of labour division and effects on national labour market structure
Bibliographic record
Abstract
Ireland’s economic growth (1992–2007) was fuelled by availability of capital, but also through access to cheap flexible labour. This article attempts to provide evidence that the Irish state played a central role in facilitating and shaping labour supply, a role that has resulted in the clustering of non-Irish workers in particular sectors of the labour market. Worker mobility across national borders takes place at the intersection of global economic trends and local or regional labour market development, thus creating global consistency in the operation of local or regional markets and demand for workers; however, each labour market is unique as each creates its own local and global social relations. The state, as a main actor in the formulation of immigration policies and in shaping labour market structure, has a central role in affecting the nature of the interconnection between global and local. The analysis considers how Ireland’s immigration policies, as they reflect global labour trends, contribute to the clustering of certain migrants in particular sectors. The method of analysis involves a three-step numerical analysis of clustering: (1) percentages, (2) ODDS ratios and (3) two-step cluster analysis. Results suggest the existence of economic clustering and channelling of workers into specific jobs linked back to immigration policies and recruitment drives.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".