Assessing the discursive foundations for emissions abatement in aviation: a post-normal science primer on alternative jet fuels
Bibliographic record
Abstract
Post-normal science was used to review the discursive foundations of environmental policy in aviation, and for reassessing the status, challenges and opportunities for alternative jet fuels to close the carbon loop of this hard-to-abate sector. The analysis revealed instances where data misrepresentation, information gaps and asymmetries, have precluded a comprehensive understanding of the environmental impacts of aviation beyond its ~2.4% global CO2 contribution. Problem representations embedded in sustainability policies and regulations, have historically understated the urgency to implement ambitious climate strategies worldwide for addressing these impacts. Out to 2050, discursive misrepresentations could prevent the air transport sector's from attaining its net-zero commitment while effecting: 1) higher carbon debts, ecosystem damage and welfare loss from unsustainable fuel production; 2) distortion of long-term market signals for alternative fuels with high sustainability profiles; 3) investment constraints for next-generation technologies; 4) increased sectoral reputational risk; 5) foremost, a continued dependence on fossil-derived fuels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".