Impact of Life-course events on hierarchical Vehicle transaction durations: Case of a Developing country.
Bibliographic record
Abstract
Vehicle ownership is a multifaceted domain of study; this paper focuses on vehicle transactions, which encompasses the temporal aspect of vehicle ownership. Life-course events have found their way into vehicle ownership models for developed countries. Owing to the high cost and unavailability of panel data, the same cannot be said for developing countries. Contrary to developed countries, vehicles owned by people in developing countries vary considerably in many aspects. The most common visible difference is the mix of two-wheelers, their varieties, and four-wheelers. In light of a prevalent hierarchy of vehicle types, people are often involved in upgrading or downgrading their vehicles. Accounting for such transactions is critical to depicting vehicle ownership behaviour accurately. To the best of the authors' knowledge, no study has attempted to understand the movement of vehicle owners along the hierarchy of vehicles owned. This study demonstrates a workaround by constructing a longitudinal dataset using retrospective mobility biography interviews. We used 420 data points from 60 interviews, with an average length of 40 minutes, to fit a competing risk-hazard duration model and find out how different life events and household structure traits affect the length of different types of transactions. Marriage, relocation, childbirth, the start of employment, and retirement were found to be the life-course events that impacted either or multiple vehicle transaction durations. Household structure attributes like prior vehicle type(s) owned, total number of workers, and number of teenagers also impact vehicle transaction durations. Although this study validates the relationship between life-course events and vehicle transactions in a developing country, aspects like heterogeneity, macroeconomic attributes, and neighbourhood attributes can be addressed in future studies. An application-oriented extension of this could be a coupling with the vehicle type choice model, making it more utilitarian.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".