Leisure mobility and individuals’ relationship to the living environment: a comparison between the Paris and Rome urban regions.
Bibliographic record
Abstract
In this article we examine how (alongside with other factors) the relationship that individuals have to their living environment affects their leisure mobility. We first elaborate a typology (comprising 5 types) of individuals according to their stated relationship to their living environment. Using a statistical approach, we then show that this typology partially explains inter-individual differences in leisure mobility, after taking into account other socioeconomic and spatial explanatory factors: income, level of education, profession, residential location (esp. density of residential area) and demographic characteristics. This statistical argument is complemented with a qualitative study of the meanings given by individuals to their living environments and leisure mobility practices, which ultimately contributes to better understand the drivers of leisure mobility and to emphasize in particular the notion of compensatory mobility. A given urban context may accomodate very different practices and very diverse life projects and the approach developed in the paper has allowed to move away from deterministic explanations for leisure mobility.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".