Activity Rescheduling Strategies and Decision Processes in Day-to-Day Life
Bibliographic record
Abstract
Results are presented from an in-depth survey of the rescheduling decision process as it occurs in day-to-day life. The multistage survey involved a preplanning interview, followed by a prompted-recall diary supported by the Global Positioning System. Subsequent comparison of preplans with executed schedules was done to identify schedule modifications, deletions, and more-impulsive additions automatically. An open-ended interview was then used to elicit the planning time horizon of rescheduling decisions, the impetus of a decision, the impact of a single decision on the rest of the schedule, and the process that subjects go through to make a decision. Responses were then compared with different sociodemographic and activity variables by using a cross-tabulation and chi-squared analysis to determine if any associations exist. Highlights of these results include the findings that adding and deleting activities requires more talking in person, that personal need is a common cause for adding shopping activities, and that the more people involved in a decision, the longer the duration of the added activity. Three conclusions can be made from these findings. First, far more rescheduling decisions and scheduling conflicts were identified compared with previous studies. Second, there were many more varied causes of rescheduling decisions beyond those typically captured in existing rescheduling conflict models. Finally, sociodemographics had little effect on aspects of rescheduling decisions compared with activity variables. Discussion of how such results relate to past conceptual frameworks is included.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".