Two-Level, Dynamic, Week-Long Work Episode Scheduling Model
Bibliographic record
Abstract
The two-level dynamic model presented in this paper was developed for scheduling work episodes within a 1-week planning period. The week-long time frame captures day-to-day variability in an individual’s work participation within a typical week. Two types of work episodes are modeled: those planned before the week (preplanned) and those scheduled during the week (unplanned). The first level of the model schedules preplanned work episodes, considering workers’ total time awake as their time budget. After the schedule of the preplanned episodes is known, the second level schedules unplanned work episodes. In this level the duration of preplanned episodes is subtracted from the first level’s time budget to define the individual’s time constraint. In each level of the framework, discrete-continuous econometric models are used to model jointly the decision of working on each day with the associated episode duration and start time. Results indicate that not only do work episodes have different attributes based on the time when they are added to the schedule but also there are interdependencies between preplanned and unplanned work episode scheduling. Working on previous days of the week increases the probability of scheduling work episodes on the following days; this setup is representative of the routine nature of much work activity. Workers with a fixed place of work schedule more preplanned work episodes, whereas they engage in fewer unplanned episodes. Flexible work duration increases time expenditure on preplanned episodes. Both models are estimated with computerized household activity scheduling survey data collected in Toronto, Ontario, Canada.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".