Socio-economic inequality in childhood and beyond: an overview of challenges and findings from comparative analyses of cohort studies
Bibliographic record
Abstract
The growing number of countries with large child cohort studies offers an unprecedented opportunity for comparative research. A topic of central interest in my research is, to what extent the sizable gaps in development that exist between children from different socioeconomic status (SES) groups in the US are also present in other countries, and to what extent the mechanisms explaining these gaps are similar or different across countries. This overview draws on the results of comparative analyses of birth cohort study data collected in Australia, Canada, the United Kingdom, and the United States, to illustrate the challenges that arise in carrying out this kind of research and the way these challenges were met - in particular those having to do with data access and comparability, and those having to do with causal inference. I conclude that this type of research also offers great promise as shown by findings on SES gaps in child development in the four countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".