Towards the Standardization of Reporting in Smartphone Travel Surveys: The Development and Application of the Smartphone Survey Reporting Guidelines (SSRGs)
Bibliographic record
Abstract
Many travel studies now include data collected through smartphones. There is a great variety in how this research has been carried out and reported. This has the potential to create problems of comparability and reproducibility in this research, very much like what happened in medicine in the 1990s. The response in medical research was the development of guidelines for authors and reviewers to follow when preparing and reviewing medical research to ensure reporting was comparable and studies reproducible. In this paper, after an extensive review of the smartphone travel survey literature, we propose an analogous set of guidelines for smartphone travel surveys, the Smartphone Survey Reporting Guidelines (SSRGs).
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 | 0.714 | 0.758 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".