Public narratives of the relationship between post-pandemic economic recovery and decarbonization: A case study of Toronto’s media sphere
Bibliographic record
Abstract
Recent research indicates that the economic downturn brought by the COVID-19 pandemic has bolstered a “climate delay” discourse. This has led environmental scholars and policymakers to express concern over how the relationship between economic recovery and decarbonization is being framed in current public discussions about post-pandemic economic recovery. To better understand how the climate delay discourse is mediated by local media and its potential impact on public support for green transformation, this article examines relevant coverage published by popular Toronto local media throughout 2020. A qualitative thematic analysis reveals a rising public demand for decarbonizing the Canadian economy. However, this demand has also been challenged by a counter storyline that seeks to divert public attention from the severe structural crisis underlying the fossil fuel sector. The study concludes by cautioning against “climate delay” narratives’ potential suppression of public support for green economic recovery.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.031 | 0.017 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".