Offshoring, outsourcing and services FDI in Europe’s ‘old periphery’ : Probing the experience of Northern Ireland
Bibliographic record
Abstract
The host economy implications of service offshoring and the ‘global shift in services’ have recently been explored in the context of various emerging economies. This study extends the literature to lagging peripheral regions of developed economies, which have hitherto been largely neglected, via a case study of Northern Ireland, a region in north-west Europe. The ‘structural characteristics’ (subsidiary attributes and network position) of services FDI projects attracted to this region since the mid-1990s are examined and some of the direct employment impacts on the host economy are considered. Two main types of operation are identified among the leading foreign investors: captive centres performing various IT-related (and other support) activities for their US parent companies in the financial service industry; and contact centres operated by foreign third-party BPO vendors, serving corporate clients in (mainly) the UK & Ireland market. The regional employment impacts of these two groups of investors have been quite different, with the former group providing fewer but higher quality, better paid and more stable jobs than the latter but also contributing to a widening sub-regional division of labour. Services FDI has partially transitioned the region from its branch plant manufacturing past but with qualitatively mixed results. Overall, this case has some interesting features that provide new insights to the service offshoring literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".