Solutions Journalism vs. Solutions-Oriented: Intentionality of Canadian Alternative Media in Building Climate Solutions Frames
Bibliographic record
Abstract
Alternative media fill a unique role in the Canadian media landscape, reaching marginalized and local communities, and as early adopters of innovative journalism practices such as solutions journalism. Solutions journalism can be an effective tool to combat audience fatigue with problem-oriented news. This is especially relevant for issues like climate change, which often receives “doom and gloom” coverage. This study aims to understand how alternative media in Canada are using solutions journalism to cover climate change and other environmental stories, via an explanatory sequential mixed-methods research design. A content analysis of all climate change and other environmental stories published by six Canadian alternative media outlets in 2022 was conducted to determine the proportion of articles using solutions journalism, and the types of solutions included. This was followed by interviews with authors of selected solutions journalism articles to further understand the frame-building process of solutions journalism. Interviews were analyzed using thematic analysis. It was found that 38% of climate and environmental articles used some degree of solutions-oriented reporting, though only 12% were fully solutions journalism. This was further explained by interview results, which identified internal and external constraints to implementing solutions journalism. Though not all journalists recalled intentionally applying a solutions journalism framework, solutions-oriented reporting is prioritized in alternative media newsrooms, is perceived as beneficial to audiences and engagement, and is made easier through newsroom support.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".