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NEETs face more difficulties with basic skills

2018· other· en· W4387649537 on OpenAlexaboutno aff

Bibliographic record

VenueOECD reviews of vocational education and training · 2018
Typeother
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
FundersEuropean Commission
KeywordsFace (sociological concept)PsychologyMathematics educationComputer scienceSociologySocial science

Abstract

fetched live from OpenAlex

We live in a world where for too long, and in too many countries, vocational education and training (VET) has been the poor cousin of national strategies to provide young people and adults with the knowledge and skills they need -and employers demand.That is why vocational education has often been seen as a provision fit only for 'other people's children' against the gold standard of academic routes culminating in university study.But evidence from countries with high performing vocational systems tells us that they provide a very effective means of integrating learners into the labour market and for opening pathways for further learning and personal growth -and there are signs that things are changing.A new wave of interest has emerged in response to rising concerns over both stubbornly high levels of youth unemployment and the unpredictability of the modern working world.Increasingly diverse and interconnected populations, rapid technological change in the workplace and in everyday life, and the instantaneous availability of vast amounts of information mean that work that can be automated or digitised can now be done by the most competitive individuals or enterprises, wherever on the globe they are located.Knowledge and skills have become the global currency of the twenty-first century, with a rising premium on those social and emotional skills that are best learnt at the workplace.So across the globe, governments are turning afresh to VET and introduced programmes aimed at enhancing its attractiveness.They aim to improve progression from VET into either skilled employment or higher level learning by harnessing the unique capacity of the workplace experience to develop skills of genuine value.This new report builds upon landmark OECD studies into upper secondary VET (Learning for Jobs, 2010) and post-secondary provision (Skills beyond School, 2014) to focus attention on apprenticeships as a uniquely important form of work-based learning.Rooted in real life workplaces, apprenticeships actively engage employers to ensure the value of skills development, but more needs to be done to ensure high-quality experiences.The aim of this report is to lift the bonnet on the design of effective apprenticeship systems.Addressing fundamental questions like the duration of an apprenticeship and how much an apprentice should be paid, the report provides a framework for policy makers and practitioners working across the world.This synthesis report follows six focused studies which were generously supported by Australia, Canada, Germany, Norway, Scotland (United Kingdom), Switzerland, the United Kingdom (Department for Education, England/UKCES, UK Commission for Employment and Skills), and the United States and the European Commission.This is an important time for vocational education.It is now widely accepted that the skills that are easiest to teach and test are also the skills that are easiest to digitise, automate and outsource.VET systems must rise to the challenge of this changing landscape if they are to remain relevant to the needs of learners and employers.This study furthers our ability to conceptualise and make sense of the changes now being encountered, enabling confident responses to emerging challenges.Looking forward, new

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)0.002
metaresearch head score (Gemma)0.005
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: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.282

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0040.006
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0840.026

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 head0.053
GPT teacher head0.397
Teacher spread0.344 · 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
Published2018
Admission routes1
Has abstractyes

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