Supplemental data for: Easy listening or driving distraction? The relationship between audiobook complexity level and driving performance on simple routes
Bibliographic record
Abstract
This dataset accompanies the study "Easy Listening or Driving Distraction? The Relationship Between Audiobook Complexity Level and Driving Performance on Simple Routes." It includes comprehensive driving performance metrics collected for each participant across all audiobook conditions. The dataset also contains results from the Operation Span (OSPAN) test, which was used to assess participants' working memory capacity. Additionally, it includes all data analyses conducted as part of the study. Driving performance metrics include measures such as lane deviation, speed variability, reaction times to external stimuli, and route completion times. Each participant's data is organized by audiobook complexity level and the corresponding driving task performance. The dataset provides a detailed overview of individual and aggregate patterns, enabling further exploration of how cognitive load influences driving performance. This research was carried out on an Oktal fixed base driving simulator equipped with 300◦ wrap-around projection displays that created a simulated driving environment. The simulator involved a Pontiac G6 convertible with its motor removed. It included all standard vehicle controls, including steering, pedals, an automatic transmission, and it collected data about all aspects of driving performance at a temporal resolution of 60 Hz. Audiobooks were played through a fully interactive media unit, and the simulations were generated using Oktal’s simulator program (SCANer Studio 1.5).
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 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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.557 | 0.147 |
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".