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Record W2905787115 · doi:10.1515/iph-2018-0015

In Podcasts We Trust? A Brief Survey of Canadian Historical Podcasts

2018· article· en· W2905787115 on OpenAlexaffabout
Nathalie Picard, Cassandra Marsillo

Bibliographic record

VenueInternational Public History · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicRadio, Podcasts, and Digital Media
Canadian institutionsCarleton University
Fundersnot available
KeywordsMythologyActive listeningTrustworthinessVariety (cybernetics)InstitutionMedia studiesPerspective (graphical)SociologyHistoryVisual artsSocial scienceInternet privacyComputer scienceArtClassics

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.090
GPT teacher head0.295
Teacher spread0.205 · 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.

Study designNot applicable
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

Citations2
Published2018
Admission routes2
Has abstractyes

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