Role of the Natural and Social Sciences in Cop21 Implementation: Success or Failure
Bibliographic record
Abstract
Hitherto, the natural sciences have furnished the essential informatio for the COP21 process, measuring the increase in greenhouse gases (GHG) and modelling the impact upon global temparatures in different scenarios of CO2:s in the atomosphere. There is still uncertainty among scientists about how strong the global warming trend is as well as how many degrees of alternative temperature rise are likely and where on the Planet. Still some scientist came forward now and deny truth of the theory of climate. However, just as important that the natural sciences deliver unbiased data and a variety of predictions is the recogniton of the major tasks of the social sciences in the COP21 framework. The COP21 will be the biggest project ever undertaken in global governancem with a budget ceiing of 100 billion dollars every year in the first half of the 21rst cenury. Implementation theory predicts complexity, reversals and the strategic handling of information. Implementation success is in no way guaranteed as each government must act in a country specific situation. Will money be forthcoming in time as well as used efficiently? Is the Stern Super Fund the powerful tool promised to poor countries for new and innovative energy policies? The purpose here is to show that most countries have an increasing lin between GDP and GHC:s as well as that they are much dependent upon fossil fuels and wood coal.
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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 | 0.261 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.029 | 0.026 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".