Businesses take stock as uncertainty goes on
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 it