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Record W2997874959 · doi:10.20380/gi2018.11

Quality 'Alone' Time through Conversations and Storytelling: Podcast Listening Behaviors and Routines

2018· article· en· W2997874959 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCanada Human-Computer Communications Society · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicRadio, Podcasts, and Digital Media
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsActive listeningBoredomStorytellingConversationComputer scienceFeelingDigital storytellingMultimediaFlexibility (engineering)Social mediaAudio equipmentPsychologyNarrativeWorld Wide WebSocial psychologyCommunication

Abstract

fetched live from OpenAlex

Audio podcasts have been widely used for more than a decade where millions of people listen to digital content on mobile devices. Despite a large amount of research on podcasts, there have not been any studies that explore the detailed listening practices of frequent podcast users, in particular, with a focus on understanding how podcasts support alone time. We conducted an interview study to understand and learn from such practices. Our results point to the characteristics of podcast technology that made it suitable for supporting people's ability to be alone yet still feel like they were connected to others. This included being able to multitask while listening to a podcast, escape from times of boredom, and even have experiential moments of self-reflection. These behaviors were supported by the flexibility of podcasts as a storytelling medium, a feeling of intimacy and connection with the podcast host, and podcasts' ability to make people feel like they are part of a conversation even when alone. We explore how these features suggest direction for technologies that can support alone time.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.057
GPT teacher head0.336
Teacher spread0.279 · 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