Twenty-first century employment and training in the countryside? The rural ‘New Deal’ experience
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».