Web versus Pencil-and-Paper Surveys of Weekly Mobility: Conviviality, Technical and Privacy Issues
Bibliographic record
Abstract
Purpose — In the context of evaluating transportation and carbon emission policies, improve weekly activity and mobility scheduling survey methodology in order to enhance data quality while reducing costs and decreasing respondent burden for designing continuous self-administered surveys that are predominantly passive (or computer-assisted).Approach — Evaluate a set of functionalities deployed in a web travel survey interface (2009) and compare with a pencil-and-paper survey (2002–2003) deployed in Quebec City that sought similar data about weekly mobility. The first used a pencil-and-paper approach complemented by interviews and telecommunications. The second used applets developed in Java, and Google Maps in order to assist geocoding of activity places and the reporting of actual trips into a relational database, while using email to recruit and support respondents.Implications — Both of these surveys had to address specific technical and privacy challenges during deployment, making their comparison relevant for discussing some of the impacts of information technologies on spatiotemporal data quality, conviviality of survey procedure, respondents' motivation and privacy protection.Limitations — While neither of these surveys employed movement-aware mobile devices, such as GPS loggers, some of the lessons learnt are relevant to the design issues raised by the increasing deployment of such devices in travel surveys, and by the growing need to manage complex surveys over extended observation periods.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".