Taiwan and Mainland Chinese and Canadian children's categorization and evaluation of lie‐ and truth‐telling: A modesty effect
Bibliographic record
Abstract
This study examined Taiwan and Mainland Chinese and Canadian children's concepts of, and moral judgments about, lying. Participants aged 7, 9 and 11 years in those locations were read stories involving child characters doing something good or bad, and telling a lie or the truth about their own deed. They were asked whether a story character's verbal statement was a lie or the truth, and whether the statement was good or bad. Results show that most children of both cultures labelled a lie as a lie, and the truth as the truth. The major cultural difference lay in children's moral evaluations of truth‐ and lie‐telling in the good deed conditions: for both Taiwan and Mainland Chinese children, as age increased, lying about one's own good deeds became increasingly positive, whereas truth‐telling about good deeds became less positive; for Canadian children, regardless of age, lying about good deeds was negative, and truth‐telling about a good deed was positive. This effect was because of Taiwan and Mainland Chinese children's increasing awareness of the need to be modest and self‐effacing in prosocial deed situations. We replicated the modesty effect of Lee, Cameron, Xu, Fu, and Board (1997) that only involved Mainland Chinese children. Given the major differences in political, economical and educational systems between Taiwan and Mainland China, the modesty effect is likely owing to children's socialization of Chinese traditional values in home and at school.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".