Bibliographic record
Abstract
and do not necessarily reflect the views of the Department for Education and Skills. All errors and omissions remain the authors. Executive Summary Education boosts individuals ’ productivity and wages. A year of extra schooling increases earnings by around 5-10 per cent. Yet many individuals leave school with minimal skills and qualifications. These individuals go on to be disadvantaged in the labour market, in terms of earnings and employment prospects. Furthermore, having a larger proportion of unskilled workers than many other developed countries puts the UK at a disadvantage economically. It has long been argued that the way for these workers to “catch up ” is through lifelong learning. Certainly, the political rhetoric has been in favour of lifelong learning both as a way to enhance individuals ’ economic and non-economic lives, and as a means of increasing the stock of skills in the labour market, and thereby making the UK more competitive. Certainly, a significant number of adults are undertaking some form of lifelong learning. For example, the National Institute of Adult Continuing Education (NIACE) 1996 survey suggested that a quarter of adults were currently learning, although estimates do vary. However, recent
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.874 | 0.859 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".