The Potential Use of Electric Bicycles in the Historic Quarter of the Seaport City of Valparaíso, Chile, Through Participatory Mapping and Focus Groups Supported by AI Data Processing
Bibliographic record
Abstract
The Seaport City of Valparaíso, Chile, declared a World Heritage Site (WHS) by the United Nations, is known for its built landscape shaped during the first phase of globalization in the late 19th century, including early transport systems. However, the city now faces growing 21st-century urban and transport challenges common in Latin America. Amid the rise of electric micromobility in Chile, this study explores the potential use of electric bicycles (E-Bikes) in Valparaíso’s historic quarter. A qualitative methodology was employed, including participatory mapping to identify strengths, weaknesses, opportunities, and threats (SWOT), and focus group discussions supported by AI-based text analysis. Findings reveal that barriers to E-Bike use extend beyond topography and infrastructure, highlighting concerns such as deteriorated public spaces, lack of green areas, and safety issues. Promoting E-Bike adoption will require improving infrastructure, launching educational campaigns, optimizing routes, and fostering community participation. This study aims to inform local decision-makers on how to enhance sustainable mobility by integrating E-Bikes into the historical electric transport of Valparaíso, thereby contributing to the modernization of mobility within a heritage context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".