A whitepaper on reforming research assessment for a digital and AI-driven science future
Bibliographic record
Abstract
This whitepaper proposes a strategic framework to advance Responsible Research Assessment (RRA) in Europe, responding to the need for a science system fit for the AI era. The initiative is rooted in the ethical principles developed by the CoARA Ethics and Research Integrity Policy for Responsible Research Assessment in Data and Artificial Intelligence (ERIP) Working Group. We introduce two co-designed tools, the Ethics Assessment Alignment Matrix (EAAM) and the Modular Assessment Configurator (MAC), to enable value-aligned, customizable evaluation workflows that integrate with open infrastructures like the OpenAIRE Graph. By fusing ethical principles with operational solutions, this initiative provides a practical pathway to embed open science directly into research and career evaluation.
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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 | 0.110 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.031 | 0.023 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".