A Bridge to Where? Tracing the Bridge Fuel Metaphor in the Canadian Media Sphere
Bibliographic record
Abstract
This paper examines how Canadian media have discussed the role of natural gas in climate change mitigation from 2016 to 2019. It also explicates different stakeholders' varying stances on the environmental impacts of North America's ongoing “shale gas boom,” as manifested in their conflicting attitudes toward designating unconventional gas as a bridge to a low-carbon future. The data in question consist of 99 articles published by Canadian media sources, all of which included explicit references to either “bridge fuel” or “transition fuel.” Through a qualitative thematic analysis, I found that more than half of the articles adopted the conventional definition of bridge fuel. Meanwhile, there are three less common, yet noteworthy interpretations arising out of the rest of the articles, which conflict with each other in terms of their views on the relationship between unconventional gas and greenhouse gas emissions reduction. Overall, the fact that bridge fuel references only appeared in a fraction of Canadian environmental and energy news reports during the target period suggests the issue's peripheral status in the Canadian media sphere. Given this situation, the paper ends by calling for more knowledge mobilization efforts to raise public awareness of the controversial factors underlying expanding unconventional gas production and consumption.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".