Using knowledge brokering to produce community-generated evidence
Bibliographic record
Abstract
Background: Devolution and integration of health and social care have placed increasing pressure on local statutory services, with a corresponding shift of health and social care to community organisations. The voluntary and charitable sector (VCS) is expected to make the case for increased funding by providing evidence of value and impact. Aims and objectives: This paper explores the challenges of compiling evidence on health outcomes which do not reflect the holistic nature of VCS support. We document how knowledge brokering can be used to enable the VCS to generate evidence. Key conclusions: Knowledge brokering (KB) may be an effective approach for developing community-generated evidence. Brokering is also needed to change perspectives on what counts as good evidence
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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 | 0.199 | 0.347 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".