The responsible innovation in health tool and the need to reconcile formative and summative ends in RRI tools for business
Bibliographic record
Abstract
ABSTRACT Responsible Research and Innovation (RRI) scholars have explored how businesses perceive the goals and processes of RRI and have developed tools to enable entrepreneurs to integrate such principles into their practices. While these tools often adopt a formative approach and may include measurable self-assessment indicators, external assessment approaches have so far received little attention. This study addresses this gap by applying the Responsible Innovation in Health (RIH) Tool, which adopts an external assessment approach, to 16 health innovations from Canada and Brazil. Combining publicly available information sources and interviews, our findings show the extent to which the nine attributes of the Tool are fulfilled and shed light on how entrepreneurs materialize these responsibility considerations. Such an external assessment increases transparency and makes more explicit the responsibility trade-offs entrepreneurs face. In view of the RRI tools available, reconciling formative and summative ends in their development could make RRI's expectations towards businesses more actionable.
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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 | 0.056 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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; both teacher heads 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".