UK's output lags in retail and financial services
Bibliographic record
Abstract
countries. An adviser from the Sector Skills Development Agency (SSDA), the Government taskforce for improving skills levels across the economy, said that there were possibly too many managers in the financial Vicki Belt, a senior research adviser at the SSDA, said: There’s a negative relationship between levels of productivity and the amount of managers. There could be too many managers in the financial services sector. Retailers were not attracting enough graduates, she said. Productivity, which is measured as output per person, grew at an annual pace of just 0.3 per cent in the second quarter this year, the weakest since 1993. By contrast, in America output per hour grew by an average of 2.5 per cent a year between 1995 and 2004. Studies have put a large part of America’s strong productivity performance down to an ITdriven surge in efficiency in retailing. A study by the Centre for Economic Performance at the London School of Economics suggested that 80 per cent of the performance gap was down to the effectiveness with which IT was used. However, the SSDA highlighted Britain’s excellent performance in those sectors, where the country came at or close to the top of the league table. Britain’s productivity levels in the manufacture of food, drink and tobacco were ranked second only to Canada. It also had the highest level of productivity in the manufacture of furniture, jewellery, musical instruments and toys.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
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".