Getting punk and personal: creating and evaluating podcasts and zines as pedagogy for teaching and learning in critical geographical methodologies
Bibliographic record
Abstract
Punk has its roots in the garage bands of the 1960s and 1970s as expressions of resistance to mainstream music. Punk, however, has evolved over time to encompass rebellion and an ethic of DIY (i.e. do it yourself) not just in music but in other domains to reject the status quo. Punk can be found in the university too. Podcasting and zines, for example, are such DIY approaches for postsecondary pedagogy. In this paper, we share our perspectives on the punk creation of a podcast series and zine associated with a course interested in engaging graduate students in critical geographical methodologies in the middle of the COVID-19 pandemic. This work came about through a synchronous online seminar that created space for graduate students to get punk and personal in their approach to engaging with course materials. We (Kiera, Saskia, Sam, and Erica) created a four-part podcast series and zine, “Getting Personal” that was submitted for evaluation in relation to the course objectives. When the course concluded, the professor (Heather) encouraged the podcasters to broadcast their DIY project. Doing so sparked mutual interest in further engagement with theory and praxis towards the work presented here; we speculate about the utility of punk projects as a pedagogical tool for uncertain futures.
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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 | 0.055 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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; 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".