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Record W4403514613 · doi:10.1080/03098265.2024.2406292

Getting punk and personal: creating and evaluating podcasts and zines as pedagogy for teaching and learning in critical geographical methodologies

2024· article· en· W4403514613 on OpenAlexaff
Kiera E. B. McMaster, Saskia N. de Wildt, Sam Mishos, Erica Shardlow, Heather Castleden

Bibliographic record

VenueJournal of Geography in Higher Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGeography Education and Pedagogy
Canadian institutionsUniversity of VictoriaQueen's University
Fundersnot available
KeywordsPedagogySociologyPunkTeaching methodHigher educationMathematics educationPsychologyPolitical scienceArt

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.125
GPT teacher head0.522
Teacher spread0.397 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

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