Brexit uncertainties and migrant labour
Bibliographic record
Abstract
In his monthly column, Professor Frank Peck, of the University of Cumbria's Centre for Regional Economic Development, looks at the difficult decisions facing workers from the EU. \n \nMuch has been said about the possible effects of Brexit uncertainties on business decision-making. In this column in last month’s issue of in-Cumbria, it was noted that uncertainties associated with impending deadlines were generating volatility in national indicators of growth arising from fluctuations in stock levels in manufacturing in particular; in the second quarter of 2019 (April to June), manufacturing output actually fell compared to the previous quarter (-0.2). The monthly figures for July released on September 9 indicate a slight bounce back for manufacturing output (+0.3 per cent) though the Office for National Statistics suggest the overall picture is mixed with only seven out of 13 subsectors experiencing growth. The overall trend still appears to show decline (manufacturing output was down 0.7 per cent comparing the three months to July 2019 with the same period in 2018). While those responsible for managing businesses face difficult decisions under uncertain circumstances, the same is true for many employees from EU countries currently working in the UK.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".