The welfare state as a context for children's development: a study of the effects of unemployment and unemployment protection on reading literacy scores
Bibliographic record
Abstract
Data were analysed from the Organization for Economic Cooperation and Development (OECD) Program for International Student Assessment (PISA) to examine whether the relationship between parental unemployment status and child reading literacy is modified by the level of unemployment protection provided by the nation. The sample consisted of 61,946 children, nested in 3,918 schools among 17 market economies. The results of multi‐level analyses indicated that, after controlling for a range of individual, family and school covariates, children with unemployed fathers in all countries had significantly lower reading literacy scores than those of employed fathers (β = −8.84, SE = 2.01). The contextual effect of unemployment protection was not significant after accounting for fathers’ employment status (β = −18.63, SE = 16.26). However, there was a significant negative interaction between unemployment protection and fathers’ unemployment, yielding the unexpected suggestion that, in countries with higher levels of unemployment protection, children with unemployed fathers fare worse, both in relation to children with unemployed fathers in lower protection countries, and in comparison with children with employed fathers (β = −26.96, SE = 8.08). Possible explanations are advanced for this result, including the potential for a ‘discouraged child effect’ arising from the potential association between unemployment protection and higher local unemployment rates (though unemployment rates at the national level were not significant).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".