Emerging psychopathology moderates upward social mobility: The intergenerational (dis)continuity of socioeconomic status
Bibliographic record
Abstract
Socioeconomic status (SES) is relatively stable across generations, but social policies may create opportunities for upward social mobility among disadvantaged populations during periods of economic growth. With respect to expanded educational opportunities that occurred in Québec (Canada) during the 1960s, we hypothesized that children's social and academic competence would promote upward mobility, whereas aggression and social withdrawal would have the opposite effect. Out of 4,109 children attending low-SES schools in 1976-1978, a representative subsample of 503 participants were followed until midadulthood. Path analyses revealed that parents' SES predicted offspring's SES through associations with offspring's likeability, academic competence, and educational attainment. Interaction effects revealed individual risk factors that moderated children's ability to take advantage of intrafamilial or extrafamilial opportunities that could enhance their educational attainment. Highly aggressive participants and those presenting low academic achievement were unable to gain advantage from having highly educated parents. They reached lower educational attainment than their less aggressive or higher achieving peers who came from a similarly advantaged family background. Growing up with parents occupying low-prestige jobs put withdrawn boys and outgoing girls at risk for low educational attainment. In conclusion, social policies can raise SES across generations, with great benefits for the most disadvantaged segments of the population. However, children presenting with emerging psychopathology or academic weaknesses do not benefit from these policies as much as others, and should receive additional, targeted services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".