The SCOPE framework – implementing ideals of responsible research assessment
Bibliographic record
Abstract
Background: Research and researchers are heavily evaluated, and over the past decade it has become widely acknowledged that the consequences of evaluating the research enterprise and particularly individual researchers are considerable. This has resulted in the publishing of several guidelines and principles to support moving towards more responsible research assessment (RRA). To ensure that research evaluation is meaningful, responsible, and effective the International Network of Research Management Societies (INORMS) Research Evaluation Group created the SCOPE framework enabling evaluators to deliver on existing principles of RRA. SCOPE bridges the gap between principles and their implementation by providing a structured five-stage framework by which evaluations can be designed and implemented, as well as evaluated. Methods: SCOPE is a step-by-step process designed to help plan, design, and conduct research evaluations as well as check effectiveness of existing evaluations. In this article, four case studies are presented to show how SCOPE has been used in practice to provide value-based research evaluation. Results: This article situates SCOPE within the international work towards more meaningful and robust research evaluation practices and shows through the four case studies how it can be used by different organisations to develop evaluations at different levels of granularity and in different settings. Conclusions: The article demonstrates that the SCOPE framework is rooted firmly in the existing literature. In addition, it is argued that it does not simply translate existing principles of RRA into practice, but provides additional considerations not always addressed in existing RRA principles and practices thus playing a specific role in the delivery of RRA. Furthermore, the use cases show the value of SCOPE across a range of settings, including different institutional types, sizes, and missions.
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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 | 0.415 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.011 | 0.108 |
| Scholarly communication | 0.030 | 0.031 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".