Because ‘everybody believes in different things’: Examining tolerance of divergent preferences, beliefs, and morals in kindergarten students
Bibliographic record
Abstract
Abstract Societies are becoming increasingly pluralistic, yet acceptance of differing points of view remains an issue. Thus, it is important to understand how young children think about issues related to acceptance of differing views and factors that may be related to sharing greater levels of tolerance. A total of 167 kindergarten students (ages 4–5) were presented with pictures of hypothetical peers and told that each peer held an opposing view from them with respect to a preference (blue, blocks), belief (believing in fairies, believing in superheroes), or moral (lying, tattling). Children were asked if it was okay for this peer to hold a different view, whether they would be willing to play with the peer, and to justify their responses. Measures of children's theory of mind understanding, language ability, and prosocial behaviour were also collected. Children shared tolerant responses more frequently towards peers who held different preferences and beliefs as compared to different morals. Children who shared intolerant responses frequently rejected the peer's different view. Willingness to play with a peer was largely based on factors unrelated to tolerance such as the peer's appearance. Children with higher theory of mind understanding and older children were significantly more likely to report that it was okay for a peer to endorse a different preference or belief, but not a different moral. Implications for children's reasoning about personal versus moral norms and the promotion of tolerance in the kindergarten years are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".