Bioenergy Discourse: A Comparison Across Media and Technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| 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 it