Bibliographic record
Abstract
The Scottish economy may have grown by its fastest rate in two years in the first quarter of 2019, but the risks to Scotland’s growth prospects have not gone away, says the University of Strathclyde-based Fraser of Allander Institute. In its latest Economic Commentary, supported by Deloitte, the research institute highlights how much of the recent up-pick in growth is likely to have stemmed from firms implementing ‘no deal’ contingency plans. Underlying growth – particularly in key sectors of the economy – remains fragile. Professor Graeme Roy, Director of the Fraser of Allander Institute, said: "Three years on from the Brexit Referendum, we still have little clarity over the timing or format of the UK’s departure from the EU. "Given the scale of the challenges that a no deal Brexit might bring, it is no surprise that businesses are taking matters into their own hands by rolling-out contingency plans. "Much of the Brexit debate has focussed upon the potential dislocation of trade patterns. But arguably an even greater challenge, particularly over the longer-term, are the implications for Scotland’s population. "Our analysis shows that a significant amount of the long-term growth gap between Scotland and the rest of the UK can be explained by differences in population growth. "Should Brexit make migration to Scotland more difficult, or less attractive, then this could have serious implications for key sectors and the economy at large."
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.043 | 0.033 |
| Insufficient payload (model declined to judge) | 0.073 | 0.036 |
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".