Navigating the Moral Landscape of Youth Development and Community Education
Bibliographic record
Abstract
This book analyses community education settings, focusing on the values that educators and practitioners impart and impose on the young people in their care. Young people participating in nonformal community education programs encounter encouragement from staff to identify with certain ideas or values: “Acquire life skills, become an entrepreneur!” “Fight for your rights!”, “Get out of the house and re-establish your essential connection with nature!”. Whether implicitly or explicitly, these messages are imbued with values. Based on a study of 12 community organizations in Canada, this book identifies four “ethical frames”: the ethical frame of the entrepreneur, of the activist, of the artist, and of the naturalist. Ilya Zrudlo argues that while each of these framings brings strengths to community education, they also carry certain ethical and educational ambiguities that render them inadequate. Drawing on the work of philosophers including Mary Midgeley, Graham Haydon, Max Weber, Hannah Arendt, Albert Hirschman, and Charles Taylor, Zrudlo offers guidance for community organizations, policy makers, and researchers when navigating the moral landscape of nonformal youth education.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| 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".