Trade versus the environment: Strategic settlement from a systems engineering perspective
Bibliographic record
Abstract
Abstract The key goal of this research is to employ a Systems Engineering approach to conflict resolution to clearly identify the ubiquitous conflict taking place at the local, national, and global levels between the basic values underlying trading agreements and those principles providing the foundations for environmental stewardship, and to suggest solutions as to how this most basic of disputes can be responsibly resolved. Subsequent to outlining the current situation involving free trade among nations and associated environmental problems, the positions of both sides in this chronic dispute between trade and the environment are summarized. Supporting the stance of free trade is the fundamental driving forces of profit maximization, while in direct opposition to this market‐driven value system are the principles of maintaining a healthy environment and related social welfare objectives. Accordingly, this global clash of values is systematically studied as a game in which the values of the Global Market‐Driven Economy (GMDE) are in confrontation with those of a Sustainable Ecosystem (SES) philosophy. A Systems Engineering tool for strategic analysis, called the Graph Model for Conflict, is utilized for realistically capturing the key characteristics of this type of complex conflict and for providing strategic insights regarding its potential resolutions. In particular, a systematic Graph Model investigation reveals that the environment and social standards will continue to deteriorate if the entrenched positions and related value systems of both camps persist. However, based on the strategic understanding gained from this formal conflict study, a number of positive proposals are put forward for resolving this conflict from a win/win perspective, at least in the long run. To highlight inherent advantages of employing a formal Systems Engineering tool for addressing strategic conflict problems, the application is used for illustrating how the Graph Model can be conveniently applied to a specific dispute and comments regarding the capabilities and benefits of the conflict methodology are provided at each step in the modeling and analysis procedure. © 2005 Wiley Periodicals, Inc. Syst Eng 8: 211–233, 2005
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".