Podcasting as a Critical Methodology: Reflections From a Social and Emotional Learning Podcasting Project
Bibliographic record
Abstract
In an attempt to exceed the constraints of academic writing and dominant approaches to inquiry, we expand in this article on an emergent arts-based methodology that utilizes voice and orality: critical podcasting methodology (CPM). We first review the paradigm of arts-based research that grounds CPM. Next, we introduce podcasting as a form of critical research and CPM as one possibility for a radical form of inquiry and knowledge creation. Following, we explain how the tenets of CPM (critical reflexivity, the dialogic spiral, voice, counternarratives, and public pedagogy) have manifested to varying degrees in our own podcasting project, Unboxing Social and Emotional Learning . We also offer considerations in the form of open-ended questions for those who are interested in conducting their own CPM projects. By sharing with transparency and humility about our ongoing podcast methodology, we hope to inspire others to value and engage with the rich possibilities that can come through podcasting.
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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 | 0.053 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.033 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".