Community listening sessions: an approach for facilitating collective reflection on environmental learning and behavior in everyday life
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it