Analysis of Research Excellence Assessment Frameworks and Providing Policy Requirements for Iran
Bibliographic record
Abstract
Purpose: Many countries have developed and implemented research excellence frameworks tailored to their specific social, cultural, and academic context. This study aims to investigate and compare national research excellence frameworks based on their objectives, indicators, levels of implementation, and evaluation processes.Methodology: This applied and library-based research was conducted using a comparative approach and the Beri model (1969). The study employed a descriptive-comparative method to analyze the structure, implementation, and evaluation mechanisms of selected frameworks.Findings Seven major research excellence frameworks were identified and examined, including Research Excellence Framework (REF), Standard Evaluation Protocol (SEP), Excellence in Research for Australia (ERA), Committee for Evaluation of Italian Research (CIVR), Canada First Research Excellence Fund (CFREF), Excellence Initiative (EI), Research Assessment Exercise (RAE). The comparative analysis revealed both similarities and differences among these frameworks in terms of objectives, evaluation indicators, levels of comparison (national, international, or both), assessment approaches (quantitative, qualitative, or mixed), scoring methods (quantitative or qualitative), and implementation processes.Conclusion: evaluating the quality and societal impact of research is essential for determining the role and accountability of academic institutions . Therefore, the development and use of comprehensive, context-sensitive indicators and metrics are necessary to assess research excellence effectively, taking into account each country’s unique requirements and policy priorities.Value: This comparative study provides an opportunity to analyze and compare national research excellence frameworks in terms of their objectives, indicators, scoring methods, approach, levels of implementation, and execution processes.
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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 | 0.241 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier 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".