What Determines the Sourcing Patterns of Multinational Plants in Ireland?
Bibliographic record
Abstract
Multinational enterprises (MNEs) have played an important, if not crucial, role in the recent development of the economy of Ireland, both North and South. This paper compares the extent to which MNE plants, North and South are integrated into their host economies through local sourcing. This is important both in terms of the direct effect on local demand but also because local trading relationships provide a medium for technology transfer and supplier development. Levels of local sourcing, North and South, have diverged sharply over the last 15 years. In the South, local supply chains have grown stronger with MNE plants now purchasing around 21 per cent of their material inputs from within the country. In the North, by contrast, local supply chain linkages have become weaker with only around 11 per cent of MNE inputs now sourced from within the region. Strong asymmetries are also identified in cross-border sourcing; Northern MNE plants spend an average of £2.4m pa in the South, while Southern MNE plants spend an average of only £0.4m pa in the North. Differences in the characteristics of MNE plants, North and South, account for only a quarter of the difference between the Southern and Northern levels
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".