Bibliographic record
Abstract
In the divided society of Israel, educators committed to the future and the well-being of young people should incorporate peace education in all the dimensions of doing and learning in the educational system. While the formal educational system does not have a peace education policy, throughout the country, many schools undertake diverse practices of peace education. However, these practices have neither succeeded in changing students’ attitudes and emotions about other groups members, nor have they succeeded in transforming conflictual relationships, between different social-ethnic-religious groups, into relationships of trust, understanding, and reciprocity. In this article, I review the accepted practices of peace education and suggest a potential explanation of the failure of these practices. The main purposes of the article are first to argue that many educators, who engage in peace education, aspire to cultivate tolerant, or even pluralistic relationships among the conflicting groups, while not engendering intercultural relationships that might ’endanger’ group identities. The second purpose is to suggest a possible solution, namely, to use humor. Using humor in peace education - in a way that is cognizant of the different cultural sensitivities - might lead to attentive dialogue among the groups and improve peace education’s effectiveness.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".