Web-Based, Crowdsourced, First-Person Narratives of Young People's Daily Commutes as a New Method for Identifying Situations Impacting Their Subjective Wellbeing
Bibliographic record
Abstract
Young people aged 15-24 represent approximately 21% of the global population and increasingly inhabit urban environments. Traditional wellbeing assessment tools typically depend on surveys that use predefined indicators failing to capture emergent, context-specific factors affecting youth navigating complex urban landscapes. This study addresses: How can we identify situations that impact the subjective wellbeing of young city dwellers during their daily commutes? We introduce “Youth-Targeted Mapped Crowd Sourced Storytelling for Wellbeing-Impacting Situation Identification” (YT-MCSST-4WISI), a novel methodology that combines Mapped Crowd-Sourced Storytelling (MCSST) for narrative collection, with a youth-targeted open-call recruitment strategy, and an analysis strategy encompassing thematic, narrative, phenomenological, and phenomenographic analyses with a focus on subjective wellbeing. We piloted YT-MCSST-4WISI via a participatory contest in Envigado, Colombia, engaging 34 ethically recruited participants aged 15-24. Using the open-source Ushahidi platform, participants submitted geotagged narratives describing their commute experiences. Narratives underwent multi-method analysis to identify recurring situations and emotional patterns. Results identified 30 wellbeing-impacting situations mostly overlooked by conventional surveys, including structural issues like steep topography (14.7% prevalence), heat exposure (23.5%), and transit unreliability, plus symbolic moments such as nature as refuge and social affirmations. By merging empathetic storytelling with scalable participatory tools, YT-MCSST-4WISI bridges constructivist and positivist paradigms, offering a scalable framework for youth-centred urban planning and policy, with strong potential for global scalability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".