Loops and Building Blocks: a Knowledge co-Production Framework for Equitable Urban Health
Bibliographic record
Abstract
This paper sets out a structured process for the co-production of knowledge between researchers and societal partners and illustrates its application in an urban health equity project in Accra, Ghana. The main insight of this approach is that research and knowledge co-production is always partial, both in the sense of being incomplete, as well as being circumscribed by the interests of participating researchers and societal partners. A second insight is that project-bound societal engagement takes place in a broader context of public and policy debate. The approach to co-production described here is formed of three recursive processes: co-designing, co-analysing, and co-creating knowledge. These 'co-production loops' are themselves iterative, each representing a stage of knowledge production. Each loop is operationalized through a series of research and engagement practices, which we call building blocks. Building blocks are activities and interaction-based methods aimed at bringing together a range of participants involved in joint knowledge production. In practice, recursive iterations within loops may be limited due of constraints on time, resources, or attention. We suggest that co-production loops and building blocks are deployed flexibly.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Science and technology studies Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.008 | 0.045 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".