Community listening sessions: an approach for facilitating collective reflection on environmental learning and behavior in everyday life
Bibliographic record
Abstract
Collaborative research approaches can promote social learning by curating a structure that facilitates inclusive dialogue and reflection. Within an epistemological frame that upholds notions of emergence rather than extraction, such modes can foster collective reflection in ways that contribute to reversing traditional notions of expertise. In this paper, we describe ‘Community Listening Sessions’, an approach drawing on focus group, learning circle, and participatory research literature. We developed Community Listening Sessions to study the interactional contexts of environmental learning – an inherently social, collective process. In our initial application, through 14 listening sessions hosted across the San Francisco Bay Area (California, USA), we engaged more than 100 community members in discussing how they learn about and take action related to the environment in their daily lives. We make recommendations for future use of Community Listening Sessions for collecting qualitative data in a participatory, equitable way in what can be challenging, high-social-cost discussions, yet those that are critical for addressing issues such as climate change, biodiversity loss, socio-environmental justice, and others that are essential to the future of our species and planet.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".