American hesitations to reduce greenhouse gas emissions: an institutional interpretation
Bibliographic record
Abstract
In 2005, the objectives of the Kyoto Protocol appear somewhat out of reach, even if Russia gave life to the protocol by signing it in 2004. Even if implemented, the protocol entails huge operational problems. Will the United States prove to be the first country to realize the difficulties in implementing Kyoto, or did they refuse to ratify it for reasons that are very particular to their own institutions? This article is an attempt at supporting the latter proposition. In March 2001, the US government announced that it was withdrawing from the Kyoto Protocol on the reduction of greenhouse gases (GHG) and it has not replaced this participation with a credible program of greenhouse gas reduction. This decision could be analyzed through different angles. In this article, we would like to look at these hesitations through an institutional angle, through the American institutions themselves. Few elements from their institutional and historical past prepare the United States to initiate a vigorous program of GHG reduction, other than through technological innovation or voluntary actions. Even though the institutional concept of path dependency is identified as the concept most helpful in explaining, from an institutional point of view, these hesitations, other institutional explanations are called upon to explain and understand these decisions.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".