Governing through community-based research: Lessons from the Canadian HIV research sector
Bibliographic record
Abstract
The “general public” and specific “communities” are increasingly being integrated into scientific decision-making. This shift emphasizes “scientific citizenship” and collaboration between interdisciplinary scientists, lay people, and multi-sector stakeholders (universities, healthcare, and government). The objective of this paper is to problematize these developments through a theoretically informed reading of empirical data that describes the consequences of bringing together actors in the Canadian HIV community-based research (CBR) movement. Drawing on Foucauldian “governmentality” the complex inner workings of the impetus to conduct collaborative research are explored. The analysis offered surfaces the ways in which a formalized approach to CBR, as promoted through state funding mechanisms, determines the structure and limits of engagement while simultaneously reinforcing the need for finer grained knowledge about marginalized communities. Here, discourses about risk merge with notions of “scientific citizenship” to implicate both researchers and communities in a process of governance.
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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 | 0.043 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.047 | 0.077 |
| Scholarly communication | 0.024 | 0.010 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| 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".