Dead-end pathways: Conceptualizing, assessing, avoiding
Bibliographic record
Abstract
Despite rising climate urgency, decision-makers continue to support emission reduction options that appear promising on the face of it but hinder progress in practice. Whether through more efficient gasoline engines or waste heat recovery from fossil fuel combustion, many proposed solutions encourage partial emissions reductions without adequate consideration of whether they can build toward net zero systems of the future. As a result, it is essential that policy decisions are interrogated in terms of their alignment with net zero pathways (or lack thereof) and that decision-makers are both informed about and held to account for the compatibility of near-term choices with long-run system change. This study conceptualizes particularly problematic directions as ‘dead-end pathways’ and outlines a framework for identifying and avoiding them. The framework assesses pathways in relation to three dimensions: depth (how close they can come to virtually eliminating emissions in a stipulated system context), breadth (how widely they can be applied across the specified system), and timeliness (how rapidly they can be deployed). The study then applies this framework to three brief case studies drawn from road transportation, each of which fail on one of these dimensions.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".