Bibliographic record
Abstract
TransCanada Corporation has proposed the Keystone XL pipeline project to transfer crude bitumen from the oil sand fields in northern Alberta, Canada, to oil refineries located in the southern part of the United States. This project has created controversy at the national level in the US and Canada and at the international level. The existence of various stakeholders with differing wants and needs has embroiled the Keystone XL in a complicated strategic dispute. This dispute was initially ignited by the potential project’s negative environmental impacts. However, economic and political issues have also played a critical role in further complicating the decision process. \nThe objective of this study is to design a strategic decision-making system for use in assessing the Keystone XL conflict with standard and perceptual graph model methods. Standard graph model analysis consists of various steps. After identifying the decision makers (DMs) subjectively, their options and preferences are determined. Then, possible scenarios or combinations of options for these DMs are evaluated. In the next step, based on rules called solution concepts, a standard stability analysis is conducted. \nThe perceptual graph model technique, on the other hand, considers the emotions and perceptions of DMs in a conflict to assess the existing dynamics among them. Although this technique takes its basic structure from the standard graph model technique, it presents unique insights into each DM’s perspectives toward the conflict and other DMs. This technique has been used in this study to understand how the awareness of one DM regarding other DMs’ perceptions can change reactions and strategies under different conditions regarding the Keystone XL conflict.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".