Bibliographic record
Abstract
New or improved decision analysis tools are developed in this thesis to address strategic policy analysis with specific focus on two topics: strategic conflict analysis and region-performance \ncomparisons. \n \nA strategic conflict refers to a situation in which two or more decision makers (DMs) are to make a decision that affects issues \nover which they have different preferences. Various forms of strategic conflict exist all around us, in areas such as environmental management, international relations, economic competition, and relationships among individuals. The graph model for conflict resolution (GMCR) is an advanced and comprehensive tool to systematically study strategic conflicts. A well-known decision tool, the analytic network process (ANP) is adapted for \nuse in strategic conflict analysis and a comparison of the performance of ANP with GMCR is carried out. Both methods are \napplied to an international trading conflict between the United States and China over the importation of television sets into the US in order to gain strategic insights about this dispute using the two different but complementary approaches. \n \nA country's overall performance comparison with respect to different kinds of indices such as economic, environmental and political indices constitutes another interesting topic for strategic policy analysis. An index aggregation approach is \nproposed to compare BRICSAM countries, a populous rapidly-growing economic group of nations consisting of Brazil, Russia, India, \nChina, South Africa, ASEAN (Association of South-East Asian Nations), and Mexico with G7 (Group of Seven), the most developed \ncountry club including Canada, France, Italy, Japan, Germany, United Kingdom and the United States. A data-envelopment-analysis (DEA) based approach is proposed to aggregate different ranking indices for BRICSAM and the G7 countries. The proposed method can provide a fair overall assessment of a country's standing by \nmaximizing its possibility of obtaining the best evaluation score. \n \nFinally, a framework to carry out generic strategic analysis for regions' competence analysis is designed based upon the theory of generic strategic analysis proposed by Porter (1980). This is a well-known approach for use in business competence analysis. The basic idea is to carry out generic strategic analysis in policy \nstudies and two decision tools, DEA and the analytic hierarchy process, are employed to quantify the analysis of competence \nefficiency and potentiality, respectively. A case study of the competence analysis of provinces in China is used to demonstrate \nthe analysis procedure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".