The Storytelling Initiative: Community Podcasting at the Frontlines of Climate and Environmental Crises
Bibliographic record
Abstract
Stories move people, and storytelling constitutes an important form of local action in the face of the multiple crises we confront at this time. Community-led podcasting is a powerful medium for conveying these stories, disrupting mass media’s dominant narrative to reframe discourse and amplify local perspectives on social and ecological issues. The Storytelling Initiative worked with frontline communities and organizations confronting climate and environmental crises to not just be subjects of podcasts, but authors and producers of their own stories. Each podcast shares a unique story of collective learning and action, ranging from youth in informal settlements building leadership to contend with climate impacts in their communities, to the use of legal mechanisms by communities in Pakistan to halt destructive government-sponsored development projects, to voices from the Arctic bringing gender relations into discussions of climate in the region. This article shares the community participatory podcasting approach used to work with the seven podcasting teams that produced these stories, and the community of practice that emerged as a result. It unpacks ways in which the stories they share push for wider change from the standpoint of their respective struggles, and invites listeners to learn with them. It also reflects on the potential of participatory podcast production and analysis as an emergent method of participatory action research.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".