Integrating the AHP and TOPSIS methods to select accounting staff
Bibliographic record
Abstract
This study aims to select accountants for a business in Vietnam. The study engaged in focused discussions with experts to establish the criteria for an accountant. Next, structured interviews were conducted with experts to collect data comparing each pair of criteria and expert scoring data for each candidate according to each criterion. Then, the Analytical Hierarchy Process (AHP) was applied to determine the weight Wj of each criterion of an accountant, and finally the TOPSIS method was applied to find the similarity coefficient with the ideal solution Ci* for each candidate selection option. The result was that candidate A1 was selected because he had the highest Ci* coefficient of 0.81479; at the same time, through the weighted results Wj of the criteria, it showed that experts highly appreciated the candidate for the following outstanding characteristics: communication skills (W1 = 0.4108), professional skills (W4 = 0.2527), and personal skills (W2 = 0.1613).
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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 | 0.038 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.005 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".