RIGHT AND WRONG FROM THE PERSPECTIVE OF 8- AND 12-YEAR-OLD CHILDREN: AN EXPLORATORY ANALYSIS
Bibliographic record
Abstract
Understanding rights not only means knowing what is permitted by law and what is not, but also being aware of and knowing one’s own rights as a human being. This analysis explores how children understand right and wrong, how they gain moral orientations and a sense of justice, and whether they are aware of human rights and children’s rights, and if so to what extent. Twelve children aged either 8 or 12 participated in an interview on a dilemma story, as pioneered by Kohlberg, and were also asked about children’s rights and human rights. Qualitative content analysis showed that the majority used moral judgements based on fairness and justice, taking the view that behaviour that is wrong should be duly punished. Eight children were able to make substantive statements on human rights; of the eight, three also had some knowledge of children’s rights. There were differences between the types of arguments used by the two age groups. There were also some differences between boys and girls, but they were negligible. In German-speaking countries there is little empirical research on children’s rights. Few studies have focused on the topic of human rights from the perspective of educational science, and there is also little research on moral concepts and children’s rights. What follows is therefore a first attempt to link two neglected research topics.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".