Environmental sustainability thinking 101: The environmental pollution production problem, global warming and dwarf green markets since 2012: Pointing out the energy future we need to construct and the one we need to avoid
Bibliographic record
Abstract
Abstract The road towards 2012 Rio + 20 was a road that was supposed to lead to the energy future we needed to build, a future towards a pollutionless world, but instead it led to a future we should have avoided, a future under ongoing dwarf green market failures. Perhaps this route was possible or it was allowed to go unchallenged because of green market paradigm shift knowledge gaps created when you shift from fully dirty economies to a fully clean economy, which hides possible transitions tools available and it makes more attractive, specially politically, to use no transition development tools; and by doing this we give a blessing of permanency to the market failures we are supposed to be trying to fix. Among the goals of this paper are: i) to show analytically and graphically, using the critical anthropocentric environmental problem-solving impossibility zone theory, how and why dwarf green market tools and thinking cannot be expected to fix the pollution production problem linked to traditional market thinking as pollution production continue to take place in the permanent environmental market failure under which they work; and ii) And then use this framework to point out the energy future we need to construct and the one we need to avoid.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| 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".