Navigating towards shared responsibility in research and innovation. Approach, process and eesults of the Res-AGorA Project
Bibliographic record
Abstract
The Res-AGorA projectRes-AGorA was a three-year, EU FP7 project (2013–2016) which has co-constructed a good-practice framework, the “Responsibility Navigator”, with practitioners and strategic decision makers. This framework facilitates reflective processes involving multiple stakeholders and policy-makers with the generic aim of making European research and innovation more responsible, responsive, and sustainable.This framework was developed based on three years of intensive empirical research comprising an extensive programme of in-depth case-studies, systematic “scientometric” literature analysis, country-level monitoring (RRI-Trends) and five broadbased co-construction stakeholder workshops.The resulting Res-AGorA Responsibility Navigator was conceived as a means to provide orientation without normatively steering research and innovation in a specific direction. Furthermore, Res-AGorA’s “Co-construction Method” is a collaborative methodology designed to systematically support and facilitate the practical use ofthe Responsibility Navigator with stakeholders. The Responsibility Navigator, the Co-construction Method and accompanying materials areready to use by actors who wish to navigate Research and Innovation towards Responsible Research and Innovation.This book provides an overview of the project’s journey, its conceptual underpinnings and main results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".