Bibliographic record
Abstract
Professor Frank Peck of the University of Cumbria’s Centre for Regional Economic Development writes for in-Cumbria on the big issues of the day and the economic data behind them. This month, the latest data on Cumbria sector employment numbers and job growth. In September 2016, employment data for 2015 was released by the Office for National Statistics. A recent review of this data from the Cumbria Intelligence Observatory makes interesting reading. Perhaps unsurprisingly, jobs in Cumbria are still dominated by five key sectors – manufacturing (38,500), health (¬31,700), accommodation & food services (¬28,400), retailing (¬27,500) and education (¬19,100). What is perhaps of greater interest is the pattern of change over time. Between 2010 and 2015, employment in the county grew by 4.4% - a net increase of more than 10,000 jobs across Cumbria. This quite striking figure mirrors national trends - a rise of 7 percent across the country as a whole. It seems that despite austerity and the economic uncertainties that characterised these years, the Cumbrian economy seemed to generate plenty of work opportunities for residents in the county. Compared with other counties of England, Cumbria’s employment performance is quite creditable. The growth rate in Cumbria may have been much below the figures for some highly dynamic local economies in the southeast, midlands and some areas of the north, in particular Cheshire and Warrington (+10%). But Cumbria could claim to be on a par with Greater Manchester (+5.1%), Sheffield City-Region (+4.2%) and surpassed other significant economies across the North including Liverpool City Region (+3.4%), Lancashire (+2.9%) and the Tees Valley -+3.7%).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.751 | 0.555 |
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".