Businesses take stock as uncertainty goes on
Bibliographic record
Abstract
In his monthly column, Professor Frank Peck, of the University of Cumbria's Centre for Regional Economic Development, looks at how growth has been boosted by firms stockpiling ahead of the original Brexit date in March and how that may harm longer term productivity. \n \nAs we enter June, the one certainty faced by business is the reality of continued political uncertainty regarding the UK’s future relationship with Europe. It is now clear that many businesses have developed strategies to manage the risks and we have seen evidence of this in the first quarter of 2019. Growth figures for January to March, announced in May, showed that the economy grew by 1.8 per cent compared to the same quarter in the previous year, its fastest rate of growth since quarter three of 2017. Closer inspection of this data shows manufacturing output, in particular, up by 2.2 per cent on the previous quarter compared with much slower growth in services of only 0.3 per cent. This stronger performance in manufacturing has been attributed in part to the effects of contingency plans of manufacturing companies leading up to the end of March 2019.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.015 |
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".