Nurturing Identity, Shaping Communities, and Forging New Pathways: Racially Minoritized Youth Climate Justice Activists’ Perspectives
Bibliographic record
Abstract
This study explores the experiences of racially minoritized youth activists involved in the climate justice movement. From July to October of 2023, I conducted semi-structured narrative interviews with 15 Black, Indigenous, and youth of colour in Ontario, aged 18 to 29, who had been affiliated with a climate justice organization for at least six months. Through timeline mapping and semi-structured interviews, participants highlighted pivotal life events that shaped their justice-oriented values. Three overarching themes emerged: nurturing identity, shaping communities and schools, and forging new pathways for racially minoritized youth leaders. The findings underscore the empowerment youth experience through local action and community engagement. With a grounding in relational solidarity and ethical relationality, this study emphasizes the imperative for Canadian education systems to integrate robust climate justice pedagogies as well as interdisciplinary, action-oriented climate justice learning that fosters student efficacy and leadership. The study also aims to highlight the ways educators, policy makers, and stakeholders can engage with climate justice, informed by racially minoritized activists.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.019 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".