Quality 'Alone' Time through Conversations and Storytelling: Podcast Listening Behaviors and Routines
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".