Decolonial Methods in Podcast Production Studies: Reflections From Practice-Led Research
Bibliographic record
Abstract
This article presents and discusses qualitative decolonial methods to research the production of podcasts. Examining podcast production as a global field of study rooted in American/Western logic we discuss how researchers may experiment with new methods and approaches to analyzing podcast production that challenge such roots. We present and discuss the use of decolonial and embodied methodologies and theoretical approaches to podcast studies as a way to promote methods of inquiry that take full advantage of podcasting as an aural medium. We ask our readers to consider questions such as, how may feminist-inspired embodied listening help us better grasp the potential of podcast productions? How can we build critical methods of inquiry for podcasting that favor intentional listening and audio coding while using qualitative data analysis software? How may we build intrinsically inclusive methodologies so that researchers can shed light on the truly diverse set of actors engaging in podcast production today? Our analysis draws upon reflections from our work as educators, scholars, and podcasters and offers a brief overview of the works of other leading scholars in the field.
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How this classification was reachedexpand
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 | 0.007 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".