Quantifying the emergence of moral foundational lexicon in child language development
Bibliographic record
Abstract
Theorists have argued that morality builds on several core modular foundations. When do different moral foundations emerge in life? Prior work has explored the conceptual development of different aspects of morality in childhood. Here, we offer an alternative approach to investigate the developmental emergence of moral foundations through the lexicon, namely the words used to talk about moral foundations. We develop a large-scale longitudinal analysis of the linguistic mentions of five moral foundations (in both virtuous and vicious forms) in naturalistic speech between English-speaking children with ages ranging from 1 to 6 and their caretakers. Using computational methods, we collect a dataset of 1,371 human-annotated moral utterances and automatically annotate around one million utterances in child-caretaker conversations. We discover that in childhood, words for expressing the individualizing moral foundations (i.e. Care/Harm, Fairness/Cheating) tend to emerge earlier and more frequently than words for expressing the binding moral foundations (i.e. Authority/Subversion, Loyalty/Betrayal, Purity/Degradation), and words for Care/Harm are expressed substantially more often than the other foundations. We find significant differences between children and caretakers in how often they talk about Fairness, Cheating, and Degradation. Furthermore, we show that the information embedded in childhood speech allows computational models to predict moral judgment of novel scenarios beyond the scope of child-caretaker conversations. Our work provides a large-scale documentation of the moral foundational lexicon in early linguistic communication in English and forges a new link between moral language development and computational studies of morality.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".