Tøffere krav? – Ferdigheter og deltakelse i arbeidslivet
Bibliographic record
Abstract
Sammendrag Arbeids- og dagliglivet har de seneste tiårene vært dominert av teknologiske endringer, særlig knyttet til informasjons- og kommunikasjonsteknologi. Ofte fremheves det at disse endringene favoriserer høyt kvalifiserte personer på arbeidsmarkedet. En følge av denne hypotesen er at jobbmulighetene for personer med svake kvalifikasjoner er forverret, og at sammenhengen mellom kompetanse og arbeidsmarkedsdeltakelse har blitt sterkere over tid. I denne artikkelen bruker vi mikrodata for Canada, Norge og USA for å undersøke om kompetanse målt ved utdanningslengde, leseferdigheter og tallforståelse har fått endret betydning for arbeidsmarkedsdeltakelse fra 1990-tallet til 2003. Vi finner ingen indikasjoner på at arbeidslivet har blitt tøffere i denne perioden, i den forstand at personer med kort utdanning og svake kvalifikasjoner i større grad faller utenfor.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.133 | 0.065 |
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".