The thorny issue of tight labour marketsWhat’s the significance of the ongoing recruitment and skills issues faced by Cumbrian businesses?
Bibliographic record
Abstract
Professor Frank Peck of the University of Cumbria asks: What’s the significance of the ongoing recruitment and skills issues faced by Cumbrian businesses? Achieving growth in the economy is now high on the agenda of the incoming Labour government. There is recognition that achieving this requires that significant barriers to growth are overcome. Of course, many of the most significant barriers are external to the firm – inflation, competition, taxation, interest rates. However, there are also significant barriers within firms, not least the ongoing difficulties experienced in recruitment and skills shortages. Nationally, labour market issues continue to be highlighted as an area of business challenge. Surveys tend to confirm that recruitment difficulties persist across the UK and in all sectors. A recent survey of more than 4,700 UK firms revealed that 59 per cent of businesses had attempted to recruit in quarter two of 2024 but 74 per cent of these had experienced difficulty in filling vacancies. The problems are particularly widespread in construction and engineering, but also affect other sectors that are prominent in Cumbria, notably in transport and logistics (79 per cent) and manufacturing (77 per cent) (British Chamber of Commerce, Quarterly Recruitment Outlook July 2024). The report concludes that labour shortages are ‘holding back growth’ for many businesses.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".