Bibliographic record
Abstract
This paper provides a framework for evaluating youth-led social change. The framework considers: seven topics (e.g., environment, human health and safety, and education); nine engagement types (e.g., volunteerism, research and innovation, and political engagement); six organizational types (e.g., advisory body, social enterprise, and individual); three strategies (socialization, influence, and power); and three scales of impacts (individual, community/inter-organizational, and national/international). Using this framework, empirical research provides evidence of how youth – defined as young people 15–24 years of age – have been agents of change in Canada over the 35 years from 1978 to 2012. A media content analysis of 264 articles, combined with frequency and chi-square tests, were completed to study the factors and the relationships among them. The results show a strong relationship between the impact and the strategy, topic, engagement type, and organizational type. The results also show a strong relationship between the strategy and the impact, engagement type and organizational type. The findings have implications for youth leaders and those who advocate for, work with, support, and educate them, and for those interested in evaluating social change efforts.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.302 | 0.101 |
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".