A Pseudo-panel Investigation of Out-of-Home Discretionary Activity Participation
Bibliographic record
Abstract
The aim of this paper is to utilize repeated cross-sectional data in a pseudo panel data approach to investigate an individual’s daily participation in out-of home discretionary activities. The pseudo panel data approach methodology using repeated cross sectional data of the General Social Survey (GSS) conducted by Statistics Canada over the period of 1992-2010 has been applied to conduct longitudinal activity participation in out-of-home discretionary activities. In this study, participation in out-of-home discretionary activities has been calculated using an individual’s participation in shopping, grocery, social, recreational, entertainment and organizational activities per surveyed year. Based on gender and the birth year of the respondent, cohorts are defined and are traced over time in each of the cross sectional data sets. A random coefficient model has been developed on the basis of the cohort data. The estimation of the random coefficient model of the pseudo panel data suggests that personal and household socio- demographic characteristics of have long run effect on out-of-home discretionary activity participations. Model results also show that there is no significant difference between male and female cohorts over the years in terms of out-of-home discretionary activity participation. Travel behavior characteristics (e.g. number of trips per day, total duration of work) also found to have significant variation between cohorts over the years. As the pseudo panel approach has been increasingly applied in travel demand analysis, thus this paper gives an insight to implement pseudo panel for activity travel participation
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".