Skill up or get left behind? Digital skills and labor market outcomes in the Netherlands
Bibliographic record
Abstract
People with low digital skills relatively often do not have a paid job, and if they do, they earn a relatively low hourly wage. Those are the most important findings of CPB research based on a newly constructed dataset combining digital skills with labor market outcomes. About a quarter of Dutch people aged between 16 and 65 does not reach a basic digital skills level. This implies that people find it hard to use email or internet and to process digital information. Based on our analysis, people who do not reach this basic digital skills level have a significantly lower hourly wage than people with good digital skills, even if we correct for background characteristics such as age, gender, educational level, literacy and numeracy. We also find that people with low digital skills relatively often do not have a paid job making them financially dependent on state benefits or a partner with a paid job. In general, people with low digital skills are older, more often female and have a low education level. Relatively often, those people are not born in the Netherlands, and they have low literacy and numeracy skills. Want to know more about people with low digital skills? Then read this ESB article (in Dutch) in which we further discuss the findings of this research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".