Summary and policy recommendations
Bibliographic record
Abstract
School and university, and the well-trod path between them, play a dominant role in thinking about education policy. But outside these two institutions there exists a less well understood world of colleges, diplomas, certificates and professional examinations – the world of post-secondary vocational education and training. Many professional and technical jobs require no more than one or two years of career preparation beyond upper secondary level, and in some countries as much as one-quarter of the adult workforce have this type of qualification (see ). Nearly two-thirds of overall employment growth in the European Union (EU25) is forecast to be in the “technicians and associate professionals” category – the category most closely linked to this sector (CEDEFOP, 2012). A recent US projection is that nearly one-third of job vacancies by 2018 will require some post-secondary qualification but less than a four-year degree (Carnevale, Smith and Strohl, 2010). The aim of this OECD study (see ) is to cast light on this world, as it is large, dynamic, and of key importance to country skill systems.
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 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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.178 | 0.054 |
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".