Session Paper Conducting Telephone Origin–Destination Household Surveys With an Integrated Informational Approach ABSTRACT
Bibliographic record
Abstract
In the urban transportation planning scene, collecting information on mobility is a costly exercise. Most of the time, it is a multi-institutional, multi-objective and multidisciplinary project. A lot of discussions arise between partisans of a very detailed and extensive questionnaire and promoters of “short and sweet”, unambiguous questions about trips made the previous day. Quality, quantity, significance, costs and nonresponse bias are legitimate issues that cannot be satisfactorily answered by a single survey method. Topics addressed in the presentation concern the demonstration of a survey method that integrates a set of technological innovations. Typically designed around the Montreal telephone household survey of 1993, the method illustrates the use of several techniques easily adapted to a standardized microcomputer environment: • Cascaded questions focused on household, people living in it and trip characteristics of these people; • Direct verification and validation of information fields and logical travel sequence (trip chaining); • Interactive geocoding of origin and destination locations, with the help of
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.070 | 0.016 |
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".