Determinants of urban cycling from the perspective of Bronfenbrenner's ecological model
Bibliographic record
Abstract
Purpose: Human behavior is complex, resulting from dynamic person-environment interactions. The study of determinants in an ecological model can be useful to understand this complexity. When it comes to bicycle commuting, previous research has identified several individual and environmental determinants that can influence behaviour and likelihood to cycle. The purpose of this article is to provide an analytical framework integrating the determinants of cycling in an analysis from the perspective of Bronfenbrenner's ecological model. Methodology: Through a literature review, we select scientific articles that include studies conducted from a variety of cities in the Americas, Europe and Asia. Findings: As a result, the article presents the determining factors for bicycle commuting in a diagram based on Bronfenbrenner’s ecological model. Research limitation: Further research, which may include a systematic or an umbrella review, could be conducted to confirm the determining factors that influence bicycle commuting in urban areas. In addition, broader work is needed to understand which factors influence the adhesion of shared bicycles and how they fit into the ecological model proposed by Bronfenbrenner. Originality: Our article provides guidelines for an analytic framework that can be a useful tool in case studies or comparative research on mobility and urbanism.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".