A typology of mismatches between skills and education using a comparative analysis between countries
Bibliographic record
Abstract
This paper aims to discuss the value of the diplomas and the situation of downgrading on the labour market. Its novelty is to compare skills both acquired and required in employment, using a self - assessment carried out by young higher education graduates a cross nine countries of Europe, Japan and Canada. More precisely, we illustrate the incidences of diploma and skill mismatches using three higher education graduate surveys, two international surveys (CHEERS, REFLEX) and a Canadian survey (NGS). We define possible over - education and skill mismatches and then present an empirical typology to show the most frequent cases of mismatches. The ideal situation which corresponds to a perfect match both in terms of diploma and skills only covers a quarter of the graduates. Norwegian and Dutch graduates are more likely to be in this situation. Our results also indicate difficulties for the different educational systems in producing the necessary skills even if a proportion of graduates are overeducated. The mismatch of certain skills is more marked that others in the typology. This is notably the case for the ability to solve problems and analytical thinking.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".