The integration of citizens into a science/policy network in genetics: governance arrangements and asymmetry in expertise
Bibliographic record
Abstract
OBJECTIVE While there are increasing calls for public input into health research and policy, the actual obtaining of such input faces many challenges in practice. This article examines how a Canadian science/policy network in the field of genetics integrated citizens into its structure and then managed their participation. METHODS Our ethnographic case study covers a 5-year period (2003-08) and combines four data sources: observations of the network's meetings and informal activities, debriefing sessions with the network's leaders, semi-structured interviews with network members (n = 20) and document analysis. RESULTS When setting up the network, the leaders wanted to include a range of perspectives (research, clinical and policy) to increase the relevance of their research production and knowledge-transfer activities. After 2 years of operation, the network's members agreed to also include citizens who were not knowledgeable in genetics and policy issues. As neither the structure nor the dynamics of the network were modified, the citizens very soon started to feel uncomfortable with their role. They doubted the relevance of their contribution, pointing to an asymmetry in knowledge between them and the expert members. There were significant tensions in the network's governance and the citizens' concerns during the process were not fully addressed. CONCLUSION The integration of citizens into transdisciplinary networks requires recognizing and addressing the asymmetry of expertise that underpins such a collaborative endeavour. It also requires understanding that citizens may feel uncomfortable adopting the pre-defined role ascribed to them, may need a space of their own or may even withdraw if they feel being used.
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 | 0.041 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.031 | 0.040 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".