Strategy Selection in the Universities via Fuzzy AHP Method: A Case Study
Bibliographic record
Abstract
SWOT (Strength, Weakness, Opportunity and Threat) Analysis, even though it enables analyzing the internal and external environment that is effective in the process of organizations and institutions to make strategic decision, is a method that has some deficiencies in terms of measurement and assessment. In order to eliminate the deficiencies of interests and make assessment through more exact data in the process of decision making, in literature, various methods under the title of quantitative SWOT Analysis has been used. One of these methods is to integrate SWOT analysis with Fuzzy Analytical Hierarchy Process (FAHP) method. In this study, the data of SWOT analysis were turned into a hierarchical structure and the model formed was solved by means of method of FAHP. The application of method was performed on the problem of strategy selection of a state university in Turkey. Surveys conducted among 1292 academic staff in the university were evaluated by SWOT analysis. For the 6 main strategies and 13 sub-strategies obtained as a result of the analyses, pairwise comparison surveys were conducted with 37 senior managers of the university. Questionnaires were analyzed by FAHP method and it was concluded that the most important strategy for the university is “to be in the country’s top 5 universities and in the world’s top 500 universities”.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".