Twenty-first century employment and training in the countryside? The rural ‘New Deal’ experience
Bibliographic record
Abstract
Introduction Since 1997 the New Labour government has incrementally introduced a raft of institutional and policy changes in relation to employment, training and skills in order to seek to boost productivity and economic growth. Cast from a mould of neoliberal political objectives, this is in part connected to constructing a knowledge-based economy (KBE) based on rising employment in financial services, high-technology and the ICT sector, media and the broader cultural economy, and the continued rise in self-employment. On another level, however, the KBE is about a new kind of labour market where deeply entrenched unemployment becomes a policy problem of the past, as those involved in the bottom-end of the labour market are actively involved in training and welfare-to-work policies to increase employability and transferable skills (see Jessop, 2002). In contrast to traditional (welfarist) social policy, as discussed in Chapter 11 of this volume, a ‘new paternalism’ is said to exist, whereby a social contract is reinforced with strict behavioural requirements and motivational engineering to increase participations in paid formal employment (Mead, 1997). Some ten years on from the inception of the New Labour government, reports published by the Leitch Review of Skills – a high-level inquiry initiated by the then Chancellor Gordon Brown – make sobering reading on the combined impacts of this regime to deliver the KBE. A historic skills deficit is highlighted and three key findings stand out: • The UK is currently ranked 17th out of 30 OECD countries in the proportion of the adult population who have low or no qualifications – with 35% at this level, which is double the proportion in the best-performing nations such as the US, Canada, Germany and Sweden. • The government's targets for achieving skills are possibly too ambitious but even if they were satisfied, “significant problems would be met with the UK skills base in 2020” (HM Treasury, 2005, p 10). • It is recognised that substantial investments by both the government and employers are being made in improving skills but the commitment needs to be more ambitious if Britain is to compete in the global economy. A key theme of the Leitch Review of Skills has been the governance mechanisms and institutional frameworks put in place over the past decade across employment and training policy sectors. It has been questioned whether there are too many agencies, partnerships and actors involved in these initiatives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".