Bibliographic record
Abstract
Across three chapters, I investigated children’s evaluations of consequences of observing fair and unfair behaviours. Chapters 2 and 3 explored whether infants and children link moral behaviours of help/harm and fairness and Chapter 4 assessed how children respond to pre-existing inequalities. My findings from Chapters 2 and 3 add to a growing body of literature that infants as early as 14-months attribute moral traits to individuals, but the novel finding would be that infants link behaviours in a bidirectional manner and this ability is stronger when the original behaviour is a moral transgression. Relatedly, Chapter 3 findings reconciled conflicting evidence in the literature that children do not reliably moral traits to make future behavioural predictions: I find that children as young as 4-years-old expected moral consistency specifically when they observed an individual perform a negatively valenced action. Finally, results from Chapter 4 provide evidence for the developmental trajectory in children’s intuitions about rectifying inequalities. With age, children recognized the context in which the inequality happened and evaluated when to correct and when to maintain or perpetuate.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".