Communicative and cultural challenges to public participation in climate action initiatives: A case study of <i>hiya</i> among Filipino immigrants
Bibliographic record
Abstract
Public participation is a critical component of climate mitigation and adaptation strategies, yet barriers rooted in diverse cultural values and communication practices remain insufficiently understood. This paper examines the Filipino cultural concept of hiya – loosely translated as “shyness”, “embarrassment”, or “shame” – to explore its influence on communication and participation. Drawing on an empirical case study of climate-focused public participation among Filipino immigrants in Calgary, Canada, the analysis uses semi-structured interviews and secondary literature to investigate how the cross-cultural communication effects of hiya shape participation in climate awareness and action. The case study reveals three key insights: first, hiya-related shyness can hinder participation at the intersection of Filipino indirect communication styles and the more direct Canadian approach; second, hiya-related embarrassment, particularly when climate science is (mis)interpreted or misunderstood, can discourage both communication and participation; and third, conscious efforts to avoid hiya-related shame, through careful management of speech and social interactions, can further suppress participation. These findings offer valuable implications for climate policymakers and practitioners seeking to foster inclusive climate action among immigrant communities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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".