Investigation of Planning Priority of Joint Activities in Household Activity-Scheduling Process
Bibliographic record
Abstract
Operational models of the household activity-scheduling process have emerged recently. These models replicate the sequence of decisions that leads to observed patterns of human activities and travel, including which activities to conduct, with whom, for how long, at what time and location, and by what mode. Activity priority has been suggested as an important dimension in such scheduling models, particularly as a determinant for the choice and sequencing of activities. The importance of intrahousehold interactions, joint activities in particular, has led to a rapid expansion of research on this topic. However, within most scheduling models, joint activities have been addressed, at best, by assuming that they are preplanned relative to independent activities. Within this context, two important issues concerning the planning of joint activities are explored: the extent to which joint activities are preplanned and whether male and female householders share the same priority when scheduling joint activities. The data set used for the study was the 2003 Computerized Household Activity Scheduling Elicitor survey for Toronto, Ontario, Canada, which recorded information about when a particular activity was planned by respondents. In the analysis, bivariate probit models are estimated for two scheduling alternatives (impulsive or preplanned) for husband and wife. Overall, the empirical results highlight the need to move beyond static priority assumptions for determining the sequencing of activities to develop a behaviorally sound model for activity scheduling. Furthermore, differences in planning priority across individual participants should be taken into account.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".