Young people not in education, employment or training (NEET), UK, Aug 2016
Bibliographic record
Abstract
The percentage of all young people in the UK who were NEET was 11.7%, down 0.3 percentage points from January to March 2016 and down 0.9 percentage points from a year earlier.Just under half (46%) of all young people in the UK who were NEET were looking for work and available for work and therefore classified as unemployed.The remainder were either not looking for work and/or not available for work and therefore classified as economically inactive. In this bulletinThis statistical bulletin contains estimates for young people not in education, employment or training (NEET) in the UK.An is available on our website.The bulletin is published 4 times article providing background information a year in February, May, August and November.All estimates discussed in this statistical bulletin are for the UK and are seasonally adjusted.The figures discussed in this statistical bulletin are obtained from the Labour Force Survey (a survey of households) and are therefore estimates, not precise figures.This statistical bulletin is accompanied by a in Excel spreadsheet format.data table Definition of young people not in education, employment or training (NEET) Young peopleFor this release, young people are defined as those aged 16 to 24.Estimates are also produced for the age groups 16 to 17 and 18 to 24 and broken down by sex. Not in Education, Employment or Training ('NEET') by age: People (seasonally adjusted)
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.019 |
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".