Backseat driver : the United States and its role in global environmental initiatives
Bibliographic record
Abstract
Thesis (M.A.L.S.)--Georgetown University, 2009.; Includes bibliographical references.; Text (Electronic thesis) in PDF format. This paper examines the role that American presidency has played in the shaping of U.S. environmental policy over the past two decades, namely in major global initiatives such as the Montreal Protocol, the Earth Summit, and the Kyoto Protocol. It employs the presidential role theories that are directly related to global policy making, such as the party leader and the chief diplomat theories. An emphasis is given to the analysis of the power relationship between the U.S. President and other chief actors, namely the United States' Congress and special interest groups. It seeks to illustrate how the power play between the executive branch and the legislative branch impacts effective or ineffective policy making. Democratic and Republican Presidents are compared with regard to their willingness to work with the legislative branch. The paper pays particular attention to Presidential leadership and effectiveness with in regard to international cooperation on global environmental initiatives and policy making.; The paper's central argument is that when the U.S. has proactive environmental leadership from its chief actors, it strengthens the multilateral efforts to address global environmental policy. Inversely, when leadership takes a back seat we will find multilateral efforts weakened. It also illustrates how domestic as well as foreign political factors play a role in shaping U.S. global environmental policy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".