Are texting and calls indeed needed while driving
Bibliographic record
Abstract
Texting and phone calls are considered as prominent types of driver distraction. All studies reviewed showed that actions involving texting significantly increase the risk of a crash. However, miscellaneous results about the safety implications of phone calls are reported. Answers of 757 Israeli respondents (57% males) to a web survey were analyzed to investigate: (1) patterns of texting and calling while driving, (2) drivers' view on the perceived risk and the need to text and call while driving, and (3) their willingness to use blocking apps which limit these smartphone usages. The results show that a high percentage of respondents use phone calls (73%, almost half do it at least frequently), and texting, illegal in Israel (35%, a quarter of them do so this continuously or frequently). We found high belief that texting compromises safety, however, this is not associated with low texting rates. Our analysis regarding the factors affecting the frequency of usage suggests that the main factor is the perceived need, while perceived safety had a non-significant effect for texting and a significant effect on phone calling. Almost half of the respondents are willing to try a blocking app regardless of the frequency of usage.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".