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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".