Balancing Anticipatory and Deliberative Governance in Public–Private Partnerships for Responsible Innovation: The role of corporate innovation capabilities
Bibliographic record
Abstract
Accelerating technological change is expanding the role of corporations in public–private partnerships for responsible innovation. While existing research emphasizes the importance of deliberative processes for responsible innovation, little is known about how corporate innovation capabilities impact such processes. Through an in-depth case study of Quayside, a Canadian smart city project, we examine how established corporate innovation capabilities shape public deliberation for responsible innovation. Our findings expose intricate challenges that arise when public entities grant corporations significant authority over innovation processes intended to be deliberative. We critically assess the effectiveness of widely embraced approaches to open innovation and human-centric design, showing that, without reflexivity, these capabilities can give rise to an imbalance between two critical modes of governance for responsible innovation: anticipatory and deliberative. Corporate self-referentiality and business interests drive anticipatory governance, reinforcing corporate expertise and promoting the instrumental use of resources and capabilities to engage citizens as consumers. When corporations lack the reflexivity needed to align this approach with expectations for meaningful public participation in a democratic context, this can derail rather than inform responsible innovation 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 | 0.035 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".