Qualitatively exploring the effect of change in the residential environment on travel behaviour
Bibliographic record
Abstract
Qualitative research with residents relocating from London was undertaken to develop an understanding of how and to what extent a change in the residential environment affected people’s travel behaviour and attitudes. Data was collected through semi-structured interviews and was thematically analysed. The findings reveal that when in a new location, residents observe the features of their built environment, identify the cause of their stress, and make efforts to address it with a change in travel behaviour. The key contribution of this study is the realisation of different levels of travel behaviour in response to a change in residential location – some residents maintained their travel behaviour, some complemented it, while some changed their behaviour to adapt to their new built environment. Theoretically, this research contributes to the extension of knowledge on travel behaviour as it focuses on suburbanising Londoners; the qualitative method adopted for this research also contributes to current knowledge. Practically, there is the potential of developing a travel behaviour change initiative around ridesharing and policy changes and initiatives to improve physical planning and sustainable travel.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".