Analyzing the characteristics of the residential relocation phenomenon through the willingness of households to move
Bibliographic record
Abstract
ABSTRACT: To face the challenges of reducing the footprint of daily travel, a sustainable solution for cities could be to propose strategies that affect the spatiotemporal structure of travel, by addressing residential location. Understanding how households choose where to live is therefore essential to help politicians and planners encourage people to select their place of residence more wisely in relation to their travel needs. This research aims to define the characteristics of households willing to move, and to examine the reasons given for this choice, using data from two Montreal CMA-wide surveys on changes in habits caused by the Covid-19 pandemic. Results show that the household typology has a strong influence on relocation reflection, with young couple households having a lower probability of wanting to stay in their housing than other household types. Proximity to services in the area of residence is also found to have a strong impact on willingness to relocate, good proximity to secondary education and employment fostering the desire to relocate, and good proximity to groceries supporting staying in current housing. Results show that getting closer to nature is the most common reason why households want to relocate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".