Sentiments toward use of forest biomass for heat and power in canadian headlines
Bibliographic record
Abstract
Replacement of fossil fuels with bioenergy, often in concert with carbon capture and storage, plays an important role in published low-emission pathways from the Intergovernmental Panel on Climate Change (IPCC) and other agencies. National and regional net-zero greenhouse gas emission commitments have caused a dramatic increase in forest biomass consumption globally, and the rise has been accompanied by debates in the scholarly literature and in society at large about the ecological and climate change impacts of forest biomass. This paper presents a quantitative analysis of media headlines about forest bioenergy published in 75 Canadian newspapers from 2010 to 2020. Using a lexicon and rules-based sentiment analysis tool, we explore negative and positive media headlines about forest biomass. Despite our finding that Canadian headlines about forest bioenergy were twice as likely to be positive as negative, media items document reversals away from forest biomass-generated domestic electricity. Our analysis found that increases in electricity costs following the introduction of forest biomass as a fuel type for Canadian electricity generation was a primary cause of these reversals. Headlines also critiqued the expanded production of wood pellets, citing forest ecological impacts and the debate about the net carbon impacts of forest biomass-generated energy. Safety issues, including stories about workplace injuries, and pellet plant fires, and economic issues, such as fiber supply and mill closures, were also featured. This research contributes a social science lens to understand perceptions over time about forest biomass for heat and power.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".