The Expanding Digital Media Landscape of Qualitative and Decolonizing Research: Examining Collaborative Podcasting as a Research Method
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.
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 | 0.245 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.013 | 0.059 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".