Podcasts as an emerging register of computer-mediated communication
Bibliographic record
Abstract
Abstract Podcasts, a relatively recent audio medium, have risen in popularity since their initial appearance in the mid-2000s. Yet, little is known about their lexico-grammatical characteristics and their relation to other computer-mediated and traditional registers. Addressing this gap, we apply Biber-style multidimensional analysis (MDA) to a representative sample of Spotify podcast transcripts and selected computer-mediated registers (e.g., informational blog, interview) as well as traditional spoken registers (e.g., broadcast, conversation). We compare their lexico-grammatical characteristics to those of other registers along the emerging dimensions. We find that, while podcasts share some linguistic characteristics with traditional spoken registers such as broadcast discussion and scripted speech, they are unlike any of the analyzed registers. In fact, their most striking characteristic is their considerable internal variability, likely related to their versatility but also due to their mixing of features and very diverse nature. In short, podcasts are an emerging register of computer-mediated communication.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".