Socioeconomic circumstances among school-aged children in Europe and North America
Bibliographic record
Abstract
Socioeconomic inequality and child well-being In today’s Europe, as well as in most other advanced countries, there is a rising proportion of people living in poverty and increasing socioeconomic inequality (Atkinson et al, 1995). This is the case, not only in the countries of Central and Eastern Europe (CEE), but also in west European welfare states and other highly developed countries such as Canada, the United States and Australia. High economic growth, in conjunction with rising unemployment and low paid jobs, has led to widening income gaps. These in turn have resulted in changing living conditions for children – for example, at least one parent out of work and resultant material hardship. In addition, in Europe and North America there have been changes in family structure and circumstances with an increase in lone-parent families, divorces and commuter families. A narrowing labour market and fragile social and family support networks place a higher proportion of children at potential risk of reduced health and well-being. The transition countries of East Europe have experienced a dramatic change of social structure within the last decade. Starting from a comparatively equal distribution of standard of living and access to life chances in the former socialist societies, the 1990’s have seen a widening gap between poor and rich families (Notburga and Wagner, 1997; Offe, 1997; Columbus, 1998). These trends are likely to affect young people’s life chances and their mental and social well-being, because of the major links between developmental potentials and life chances. However, the impact of social inequality during childhood and adolescence, in terms of impairment to health and well-being rather than mortality or morbidity, has received limited attention in cross-national studies of young people. In their recent analysis, Micklewright and Stewart (1999) identified the need for more measurement of child well-being, in order to address this dearth of data. One cross-national study of young people in West and East Europe and in North America has attempted to measure childhood and adolescent socioeconomic circumstances and investigate their association with health and well-being. This is the Health Behaviour in School-Aged Children: WHO Cross-National Study (HBSC) (Currie et al, 1998; Currie et al, 2000). A description of the study and its methodology is given in the Appendix.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".