Public Engagement, Public Consultation, Innovation and the Market
Bibliographic record
Abstract
Engagement of the public regarding new science and technology is almost a routine feature of the innovation cycle in Canada. Recent examples include: the Health Canada-initiated consultation on xenotransplanation, public engagement in self-standing GE3LS research programs or projects embedded in scientific platforms, the launch of the National Research Council’s e-democracy laboratory, and the Canadian Biotechnology Secretariat’s rolling study of consumer attitudes toward biotechnology. The vast majority of research into the methods, effectiveness, and merits of public engagement is conducted by, or with the assistance of, university-based researchers, the major exception being opinion polls conducted by professional pollsters. In order to fulfill the requirements of university-based research, academics collect kudos from like-minded peers by presenting their results at conferences and publishing in academic journals and books. Often the research is also deliberately or derivatively disseminated into the grey literature for use by industry and the public service. In this dissemination mode, researchers are often regarded as consultants who provide nonacademic constituencies with expert advice on assessment of public attitudes toward new science and technology.
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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 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".