Voices of Canadian and Mexican youth surrounded by violence: Learning experiences for peace-building citizenship
Bibliographic record
Abstract
How do young people living in high-violence contexts express a sense of democratic agency and hope, and/or frustration and hopelessness, for handling various kinds of social and political conflict problems? The management of conflict is a core challenge and purpose of democracy, severely impeded by the isolation and distrust caused by violence. Publicly funded schools can be (but often are not) part of the solution to such challenges (Bickmore, 2014; Davies, 2011). This article is drawn from a larger on-going project probing the (mis)fit between young people’s lived citizenship and conflict experiences, and their school-based opportunities to develop democratic peace-building capacities, in non-affluent local contexts surrounded by violence, in an international comparative perspective. We report on focus group conversations with several small groups of students, ages 10–15, in two Canadian and four Mexican schools in marginalized urban areas. Diverse participating young people tended to have a stronger sense of agency and hope in relation to some kinds of conflicts (such as environmental pollution) compared to others (such as unemployment and insecure work or drug-gang violence). In general, they did not feel that their lived citizenship knowledge was much valued or built upon in school.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".