Do key informants and commuters share the same thoughts on modal shifts? Reflection from in-depth interviews conducted during COVID-19 in Dhaka, Bangladesh
Bibliographic record
Abstract
In this reflective praxis, we share our experience of conducting in-depth interviews with key informants and commuters’ in Dhaka, Bangladesh. We conducted the study in 2020 and explored the perspectives of health, transport and urban planning practitioners and young commuters in Dhaka on potential transportation mode shifts amid COVID-19. From our experience and observation, we saw that commuters emphasized the barriers and challenges they face during the pandemic which key informants also acknowledged. On the other hand, health professionals were more specific on the underlying reasons behind possible transmission risks than commuters. Additionally, key informants shared an abstract and theoretical view of the potential of mode shift, which would appear to be influenced by their formal knowledge of European cities’ transportation policies and strategies rather than their lived experiences. Our understanding is that there is a difference in the thought process between key informants and commuters based on how they experienced the transportation system and their knowledge of other systems and thus how they defined the transportation problem and possible solutions.
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".