Further and higher education and skills
Notice bibliographique
Résumé
The situation on the eve of the crisis In December 2006, six months prior to Gordon Brown's new ministerial team taking office, the Leitch Review of Skills (2006) set out an analysis of the challenges the government faced. Using qualifications as a proxy for skills, Leitch argued that the UK's skills base had improved significantly. Between 1994 and 2005, the proportion of people with a qualification at Level 4 (sub-degree level) or above had risen from 21% to 29%, and the proportion with no qualifications had fallen from 22% to 13%, while 42% of those aged 18-30 were participating in higher education (HE), more than ever before. The number of apprentices had more than trebled since Labour took office in 1997. However, other countries had also been improving their skills, often from a higher base, so the UK's skills base was mediocre by comparison with international competitors. The proportion of people with no or low qualifications was more than double that in Sweden, Japan and Canada. Youth unemployment was already rising, even during the boom years of the 2000s, and the proportion of 16- to 18-year-olds not in education, employment or training (NEET) hovered steadily around the 9 to 10% mark, despite rising school attainment. Post-16 participation in education and training was below the OECD average. Fewer than 40% of people were qualified to intermediate level, compared with more than 50% in countries such as Germany and New Zealand. The situation for high skills was better, around the international average, but the UK was investing substantially less in higher education than leading competitors, and being overtaken by countries that were improving their participation rates faster (OECD, 2010). Thus, although the UK was in a strong economic position, with a comparatively high employment rate and sustained economic growth, its competitiveness was increasingly at risk, with productivity lagging well behind countries such as France, Germany and the US. Leitch argued that improving skills was central to achieving a fairer and less unequal society: unequal access to skills had contributed to high rates of child poverty and income inequality, and there were clear links between skills and wider outcomes such as health, crime and social cohesion.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,010 | 0,006 |
| Science ouverte | 0,001 | 0,008 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,103 | 0,017 |
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 source (Gemma direct ou Codex distillé), 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 ».