Performance measurement in governmental agencies using BSC-AHP: A case study of Civil Registry Office in Tehran
Bibliographic record
Abstract
Measuring the performance of governmental organizations plays an important role on increasing public satisfaction in any society. One of the effective models for assessing the organizations performance is balance scorecard (BSC) model, which investigates all aspects of organizations. In this paper, we use a hybrid of analytical hierarchy process along with BSC to measure the performance of five different civil registry offices in Tehran, Iran. We use fuzzy terms to handle uncertainty in input numbers and using some technique convert fuzzy numbers into crisp values. The results of our survey indicate that learning and development is number one priority with relative importance of 0.491, followed by customer with relative importance of 0.293, internal process with relative importance of 0.173 and financial affairs comes at last with relative weight of 0.043. The study uses organizational researchers, training, quality, customer satisfaction, performance measurement, expenses and annual budget as major components for analyzing five regions. We have also performed sensitivity analysis to see the effects of different changes on ranking.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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; 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".