Children's Understanding of Intentional Causation in Moral Reasoning About Harmful Behaviour
Bibliographic record
Abstract
When evaluating a situation that results in harm, it is critical to consider how a person’s prior intention may have been causally responsible for the action that resulted in the harmful outcome. This thesis examined children’s developing understanding of intentional causation in reasoning about harmful outcomes, and the relation between this understanding and mental-state reasoning. \n\tFour-, 6-, and 8-year-old children, and adults, were told eight stories in which characters’ actions resulted in harmful outcomes. Story types differed in how the actions that resulted in harm were causally linked to their prior intentions such that: (1) characters wanted to, intended to, and did perform a harmful act; (2) they wanted and intended to perform a harmful act, but instead, accidentally brought about the harmful outcome; (3) they wanted and intended to perform a harmful act, then changed their mind, but accidentally brought about the harmful outcome; (4) they did not want or intend to harm, but accidentally brought about a harmful outcome. Participants were asked to judge the characters’ intentions, make punishment judgments, and justify their responses. Additionally, children were given first- and second-order false-belief tasks, commonly used to assess mental-state reasoning. \n\tThe results indicated that intention judgment accuracy improved with age. However, all age groups had difficulty evaluating the intention in the deviant causal chain scenario (Searle, 1983), in which the causal link between intention and action was broken but a harmful intention was maintained. Further, the results showed a developmental pattern in children’s punishment judgments based on their understanding of intentional causation, although the adults’ performance did not follow the same pattern. Also, younger children referred to the characters’ intentions less frequently in their justifications of their punishment judgments. \n\tThe results also revealed a relation between belief-state reasoning and intentional-causation reasoning in scenarios that did not involve, or no longer involved, an intention to harm. Further, reasoning about intentional causation was related to higher-level understanding of mental states. The implications of these findings in clarifying and adding to previous research on the development of understanding of intentional causation and intentions in moral reasoning are discussed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".