In Podcasts We Trust? A Brief Survey of Canadian Historical Podcasts
Bibliographic record
Abstract
Abstract In this article, we highlight the exciting and growing historical podcast scene in Canada. We chose a variety of podcasts to represent the diverse institutions, communities and individuals who are telling histories through this medium. To represent popular history, we looked at Our Fake History a project that delves into historic mythologies and conspiracies. For the academic perspective, we looked at Active History , produced by Sean Graham of Carleton University, and at the museum-based podcast, Kitchen Stories , from the Jewish Archives of British Columbia, as an example of institutionally produced media. Community podcast The Nameless Collective and student-run podcast 3600 secondes d'histoire round out our survey. Each podcast shows a different approach to telling history, and allowed us to explore the issue of authority. Asking the question, “Can we trust historical podcasts?”, we examine how each podcasters establish their relationship to their audience, and conveys their expertise on the topics they discuss. Regardless of the perceived level of formal authority, from individual to institution supported podcaster, we found that trust was formed primarily through the intimate listening experience. Listeners are invested in keeping the podcasters accountable and therefore help produce trustworthy historical podcasts.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".