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Record W4205275998 · doi:10.46692/9781847423610.013

Twenty-first century employment and training in the countryside? The rural ‘New Deal’ experience

2008· other· en· W4205275998 on OpenAlexaboutno aff
Suzie Watkin, Martin Jones

Bibliographic record

Venuenot available
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)Rural areaPolitical scienceEconomic growthGeographyEconomicsLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.012
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0200.002

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.

Opus teacher head0.021
GPT teacher head0.229
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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