Design of a Strategic-Tactical Stated-Choice Survey Methodology Using a Constructed Avatar
Bibliographic record
Abstract
This paper presents findings from a small-sample qualitative study on people's activity travel behavior in the presence or absence of carsharing. A carsharing service provides its subscribers with short-term access to a fleet of shared cars. In previous research, subscribers have reported distinctive travel patterns, such as more car usage by subscribers than by non–car owners but less than by car owners. Reflexive techniques were used in which interviewees adapted a week of their recent activity travel behavior in response to stimuli. Findings from this study informed the design of a stated-choice survey that addressed three principal forms of complexity: (a) strategic-tactical choice situations, (b) situations in which respondents might select multiple interacting options in a single choice situation, and (c) situations in which sufficient knowledge of the individual survey respondent to tailor such a complex choice situation could not feasibly be gathered during a single interview. In the proposed design, the respondent indicates a strategic choice of which methods of travel to make available for use given a set of representative out-of-home activities. Accessibility to each activity by various means of travel is generated by using empirical distributions from Britain's National Travel Survey data sets to maximize plausibility of the information presented to respondents. An avatar (a virtual character for purposes of the survey) is constructed for each respondent on the basis of a small set of self-reported demographic characteristics. The use of multiday activity travel diaries would ideally involve multiple points of contact with each respondent at substantial cost. Therefore, an alternative method involving a single interview per respondent was sought.
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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 | 0.047 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".