Bioenergy Discourse: A Comparison Across Media and Technologies
Bibliographic record
Abstract
This study compares the discourse surrounding bioenergy with carbon capture and storage (BECCS) and sustainable aviation fuel (SAF) across two media: social media and academic literature.Through an automated content analysis of Twitter/X posts (n = 11,314) and peer-reviewed articles (n = 140), we identified significant differences in the prevalence of techno-optimism, techno-skepticism, and engagement with critical issues related to socio-environmental impacts and technological uncertainty for these bioproducts.The findings reveal that social media content is generally more optimistic and less critical of these technologies compared to the academic literature, with a notable lack of discussion on the potential social and environmental consequences.Furthermore, our analysis highlights a greater polarization of views in relation to BECCS, with both techno-optimism and techno-skepticism being more prominent across both media.The study emphasizes the importance of effective science communication, balanced evaluations of risks and benefits, and closer collaboration between academia and businesses to foster a more informed and nuanced discourse on disruptive technologies in the bioeconomy.Our findings also emphasize the need for scholars and businesses operating in the biomaterials and bioproducts industry to adopt a critical approach to media literacy.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".