Citizen expectations of ‘academic entrepreneurship’ in health research: public science, practical benefit
Bibliographic record
Abstract
BACKGROUND: Responsiveness to citizens as users of technological innovation helps motivate translational research and commercial engagement among academics. Yet, retaining citizen trust and support for research encourages caution in pursuit of commercial science. OBJECTIVES: We explore citizen expectations of the specifically academic nature of commercial science [i.e. academic entrepreneurship (AE)] and the influence of conflict of interest concerns, hopes about practical benefits and general beliefs. DESIGN, SETTING AND PARTICIPANTS: We conducted a cross-sectional national opinion survey of 1002 Canadians online in 2010. RESULTS: Approval of AE was moderate (mean 3.2/5, SD 0.84), but varied by entrepreneurial activity. Concern about conflict of interests (COI) was moderate (mean 2.9/5, SD 0.86) and varied by type of concern. An ordinary least-squares regression showed that expectations of practical benefits informed support for AE, specifically that academic-industry collaboration can better address real-world problems; conflict of interest concerns were insignificant. CONCLUSIONS: These findings suggest that citizens support AE for its potential to produce practical benefits, but enthusiasm varies and is reduced for activities that may prioritize private over public interests. Further, support exists despite concern about COI, perhaps due to trust in the academic research context. For user engagement in research priority setting, these findings suggest the need to attend to the commercial nature of translational science. For research policy, they suggest the need for governance arrangements for responsible innovation, which can sustain public trust in academic research, and realize the practical benefits that inform public support for AE.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".