Strategy Selection in the Universities via Fuzzy AHP Method: A Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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”.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it