Bibliographic record
Abstract
An experiment that used an application of a pragmatic smartphone travel survey developed to minimize respondent burden while collecting primarily passive data between destinations is described; invited participants came from known population, Concordia University. Respondent burden was reduced by optimizing battery usage, requiring little from respondents apart from downloading and installing an app, completing a short survey, and allowing the app to run in their smartphones’ background. The experiment showed that a surprisingly large number of people (892) contacted by e-mail were willing to participate in the study, with a resultant surprisingly large amount of data as well (4,154 respondent days). Moreover, the overall age distribution of the sample was found to be closer to the true population than a traditional origin–destination (O-D) survey capturing the same population. Differences in travel behavior results from the O-D survey appear plausible given what is known about both smartphone and traditional surveys. That respondents were not asked to validate their data reduced respondent burden, but some validated data are necessary to derive meaningful information from collected data. The collection of some less accurate data when GPS is not available is an important avenue to reduce the identification of missing trips. The authors view this experiment as a data point, among others, in attempts to understand the trade-offs involved in the development of smartphone applications. The authors hope it will contribute to the use of such applications on a larger scale in data collection initiatives.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".