Productivity performance varies considerably across the regions of the UK and in recent years, the productivity gap between the UK and Cumbria has widened
Bibliographic record
Abstract
There is a long-standing problem with labour productivity in the UK economy. Over several decades, various measures of productivity show a significant gap with major competitors. In 2021, for instance, while levels of output per hour were above those in some countries (eg Italy, Canada, Japan), the UK was 19 per cent below the USA and 15 per cent lower than both Germany and France (House of Commons Library – Briefing No 02791, Economic Indicators, May 2023). These significant gaps have not only existed for some time, but there are few signs of them closing. Preliminary estimates published by National Statistics show that in the first quarter of 2023, UK output per hour was actually 0.6 per cent lower than in the same quarter in 2022, resulting in the weakest annual growth rate since 2013 (excluding the effects of the pandemic). Why does this matter? Fundamentally, weak productivity growth affects economic performance relative to competitors elsewhere. It also constrains the ability of employers to offer higher wages and therefore has negative consequences for living standards. On a more positive note, for some employers measures to increase productivity can be a means to address current labour shortages.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".