A narrative model for exploring climate change engagement among young community leaders
Bibliographic record
Abstract
INTRODUCTION: Decades of widespread knowledge about climate change have not translated into adequate action to address impacts on population health and health equity in Canada. Research has shown that context-based perceptions and interpretations mediate engagement. Exploring climate change engagement involves inquiry into contextual experience. METHODS: This qualitative study has employed narrative methodology to interpret the meaning of climate change among community leaders in Saskatoon, Saskatchewan, Canada, age 20-40 (n = 10). Climate change narratives were explored both structurally and thematically. RESULTS: A model was developed to organize results and to describe concepts of fidelity and dissonance within participant narratives. Findings suggested that knowledge of climate change and personal motivation to act did not preclude narrative dissonance, which served as a barrier to a meaningful personal response. Dissonance can result where internal and external barriers mediate mobilization at moments in the plot: (1) moving from knowledge of the challenge to a sense of agency about it; (2) from agency to a sense of responsibility to choose to address it; (3) from responsibility to a sense of capacity to produce desirable outcomes despite contextual challenges; and (4) from capacity to a moral sense of activation in context. Without narrative fidelity, meaningful mobilization can be hindered. CONCLUSION: A narrative model is useful for exploring climate change engagement and highlights opportunities for a population health approach to address the conditions that hinder meaningful mobilization. By framing climate change narratives with emotional and moral logic, population health framing could help young leaders overcome internal and external barriers to engagement.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".