Depoliticizing And Humanizing Climate Change Through A Public Health Frame: A Case Study Of Ontario’s Climate Change Policy Discourse
Bibliographic record
Abstract
The relevance of a public health frame in supporting the climate change impact awareness and consensus on actions is well recognized but largely underutilized. Overall, supporting public health’s capacity in climate change has focused on projecting and highlighting public health impacts due to climate change, identifying public health policy responses, and emphasizing public health role. The integration of the public health perspective in the discussion and communication of climate change ideas has remained elusive. Climate change is also a complex social problem whose construction of meaning and actions is rooted in institutionalized language, discourse, and human interactions. Thus, understanding of the construction of the relevance of public health in climate change discourse is central to understanding the impediments of the public health frame application. Unfortunately, this has been a neglected area of research, and the dissertation responded to that gap. To delineate the impediments of the public health frame, the study used the case study of the context of climate change policy discourse in the Province of Ontario (Canada) to examine the construction of public health relevance, the extent of public health frame application, and the systematic influences in the discourse. The analysis of policy documents and key informant interviews revealed that the public health frame remained isolated from the primary focus of Ontario’s climate change policy discourse. Instead, Ontario’s historically and socially constructed climate change as an economic and political issue solved through market strategies and technological innovations forwarded by political, bureaucratic, and technological elites. The focus substantiated the types of structures and processes of policies and decisions, the relevant actors and knowledge, and the values supporting the discursive, normative, and strategic practices. Ontario’s focus also limited the utilization of the public health frame and the supporting capacities through the misalignment between public health and the provincial strategic actions, the lack of recognition and integration of public health roles, mandate and structures, and limited public health capacity building initiatives. Therefore, public health framing as an endpoint of climate change discourse requires legitimation of public health in the underlying institutional structures for, and governance of, climate change.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.042 | 0.021 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".