Multi-Criteria Decision Making Approach Regarding the Choice of University for Postgraduate Studies
Bibliographic record
Abstract
The problem set in this paper regards how to make the choice of University for postgraduate studies. The multi-criteria decision making approach is suggested to be used for solving this problem. The idea is to decompose the problem into the following elements: goal-choice of University for postgraduate studies; criteria that contribute to achieving the goal and that can be of quantitative or qualitative nature; and alternatives-Universities that the choice will be made from. For such problem a hierarchical model can be built, and it can be solved by using the analytic hierarchy process (AHP). The objective of this research, which will be conducted in the form of a questionnaire among the best fourth-year students from Ss. Cyril and Methodius University in Skopje, Faculty of Economics-Skopje, is to determine how many of them will continue their education in postgraduate studies, and where, as well as to identify the criteria that are important in their opinion when choosing a University. Once the participants’ answers are received, two groups of criteria that are important regarding the choice of University (in Macedonia and abroad) will be created, and thus two groups of participants will be formed. The choice of criteria will be made according to the arithmetic mean, and if the number is high then factor analysis will be used for their reduction. Afterwards, the participants will be introduced to the AHP method and for combining the individual judgments in group judgment; the geometric mean will be used. The University that is the best choice for each of the participants will come as a result of the ranking of the overall priorities of the alternatives.
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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 | 0.025 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".