The Expanding Digital Media Landscape of Qualitative and Decolonizing Research: Examining Collaborative Podcasting as a Research Method
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
Technology of the twenty-first century has transformed our ability to create, modify, store, and share digital media and, in so doing, has presented new possibilities for how social science research can be conducted and mobilized. This paper introduces the use of collaborative podcasting as a research method of critical inquiry and knowledge mobilization. Using a case study, we describe the methodological process that our transdisciplinary team engaged in to create the Water Dialogues podcast, a collaborative initiative stemming from a larger research project examining approaches to implementing Indigenous and Western knowledge in water research and management. We situate collaborative podcasting within an expanding field of collaborative and participatory media practice in social research, and consider how the method may align with and support research within a decolonizing agenda.
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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 | 0.037 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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