SHORT EDUCATION INTERVENTION MITIGATES AGEISM IN EARLY ADOLESCENCE
Bibliographic record
Abstract
Abstract This study explored the effectiveness of a 90-minute workshop in reducing ageism and fostering social activism among 318 Israeli teenagers (11-15 years old, 73.4% female). The workshop aimed to provide accurate information, counter-stereotypes, and peer discussion through activities like talks, games, videos, and meme creation to promote a more inclusive society. The intervention significantly improved familiarity with the concept of ageism, with teenagers drawing parallels to discrimination and racism. The Children’s Attitudes Towards Elderly (CATE; Seefeldt et al., 1977) was administered before and after the intervention. A dependent samples t-test indicated a significant mean difference in age stereotypes within the same participants, with a medium effect size, (t(317)=-11.91, p<.001, Cohen’s d=.51). Building on prior findings, this study adds to the evidence that educational interventions can improve attitudes towards older adults. Interestingly, while a wider range of age stereotypes emerged post-workshop, a shift towards positive perceptions was observed, particularly among females. Importantly, two-thirds of the teenagers’ created memes targeted ageism against older adults, highlighting its prevalence. However, nearly 20% promoted age-inclusivity, and 17% addressed ageism towards younger generations. These findings suggest the intervention’s potential in fostering social change and highlight the need for further exploration of ageism in youth populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".