Fraser of Allander Institute : Economic Commentary [December 2017]
Bibliographic record
Abstract
This week's Scottish Budget (14.12.17) comes at a crucial time for Scotland's economy. With Brexit uncertainty continuing to cast a shadow, plus a gloomier outlook for UK productivity, the Budget provides an important opportunity for the Scottish Government to set out their plans to support the Scottish economy. The Budget will also mark the first time that we will have forecasts from the new Scottish Fiscal Commission (SFC). The SFC will provide an independent assessment of the outlook for the economy, devolved taxes and social security. They will do so against a backdrop of ongoing economic fragility. Growth in Scotland slowed to just 0.1% over the 3-months to June. Over the year, growth has been around 1/3 that of the UK. In contrast, employment continues to be close to a record high – at least since the Labour Force Survey started in 1992. The downside has been further falls in productivity. The latest leading indicators suggest that the economy is continuing to grow, albeit at a relatively slow pace. The Scottish FAI/RBS Scottish Business Monitor for Q3 2017 showed both a rise in business and new orders. Our latest survey of activity in the oil and gas sector shows a further pick-up in optimism, although conditions remain challenging.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.022 | 0.014 |
| Insufficient payload (model declined to judge) | 0.130 | 0.045 |
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".