Investigating multiple activity participation and time-use decisions by using a multivariate Kuhn-Tucker demand system model
Bibliographic record
Abstract
This paper investigates time allocation behavior in activity planning. Considering a 7-day planning period, the paper empirically investigates time allocation behavior with respect to non-skeletal activity types. Non-skeletal activities indicate all activities except work/school activities. Activities under consideration are classified into 15 generic types and in addition to these; the econometric method used in this paper allows consideration of all other undefined/unplanned activities as a ‘composite activity’ within the time-budget. The concept of activity utility is used to model the perception of individual activity types. Activity-type indicator variables are used to investigate inter-activity relationships in baseline preference and time allocation. CHASE survey data collected in Toronto are used to estimate the empirical model. All parameters of the model are considered to be distributed multivariate normal. Bayesian estimation technique is used to estimate the large number of parameters resulting from the multivariate distribution assumption of the parameters. The estimated model reveals considerable behavioral insight into the time allocation among different activity types.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".