Stated Adaptation Survey of Activity Rescheduling
Bibliographic record
Abstract
This paper describes an analysis of activity rescheduling responses to an unexpected 1-h delay in getting to an activity. To focus on this rescheduling scenario, a stated adaptation survey technique was developed in which the hypothetical scenario of an unexpected 1-h delay was introduced to a randomly chosen activity from the observed 2-day executed activity schedules of respondents. This paper describes the survey technique used to elicit stated adaptation responses to this conflict, an exploratory univariate analysis of the explanatory factors, and the results of a simple multinomial logistic regression model that attempts to explain the combined effects of a variety of explanatory variables. Finally, a comparison is made with other revealed preference data from an interactive scheduling process survey to shed light on the differences in rescheduling strategies for activity conflicts without forewarning. Attributes of the activity are the major influences behind the type of rescheduling actions taken. The only person and household attributes found to influence the response significantly are the involvement of children in the activity and the student status of the individual for whom the delay occurs. Schedule attributes are not found to have a significant effect. Some new evidence suggests that when conflicts occur with little forewarning, people are less likely to skip or change the day of the activity and more likely to shorten it, where possible, or shift it to another part of the day.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".