Unitary Social Science for Causal Understanding: Experiences and Prospects of Life Course Research
Bibliographic record
Abstract
Longitudinal data are superior to cross-sectional data for explaining social processes. Yet, the existing division of labour in social science is a serious handicap for causal understanding of human behaviour. This is demonstrated in this article with the quite unrelated coexistence of sociological research on life histories and psychological research on individual development. Two examples are discussed: the intergenerational reproduction of social inequalities and the openness versus closedness of labour markets. Though there is an increasing awareness of problems of selectivity and unobserved heterogeneity in conventional social research, statistical modelling of these problems cannot replace the need for transdiciplinary data collection and research.
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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 | 0.104 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.100 |
| Scholarly communication | 0.014 | 0.040 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".