Audiobook Stylistics: Comparing print and audio in the bestselling segment
Bibliographic record
Abstract
The paper explores differences between bestsellers in print and the most popular audiobooks in a subscription-based streaming service for books ('beststreamers') by means of computational stylistics. The point of departure is the complete set of print bestsellers and digital audiobook beststreamers for the Swedish book market 2015–2019, in total 172 novels. We probed 34 linguistic measures to track differences between subsets at the stylistic level. The results indicate that there are pronounced differences between the formats. Print bestsellers are longer, syntactically more complex and varied, and seem to focus more on depiction. Beststreaming audiobooks, by contrast, are shorter, more straightforwardly written, and appear to highlight plot and dialogue. The results are replicated when the comparison is restricted to crime fiction, the most prominent genre in the commercial top segment. Given these results, it is argued that it is possible to discern a particular audiobook style as one factor affecting book consumption in digital formats, and conversely that the printed format is associated with other stylistic preferences.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".