Knowledge brokering: (mis)aligning population knowledge with care of fat bodies
Bibliographic record
Abstract
OBJECTIVE: Two prominent Canadian knowledge brokers aim to influence how primary care clinicians address obesity, through the dissemination of texts: the Canadian Task Force on Preventive Health Care (guideline) and the Canadian Obesity Network (5As). While written for the same clinician and adult patient population, the recommendations differ. This analysis highlights active decisions that produced the difference. METHODS: Frame analysis of the guideline and 5As texts. RESULTS: The brokers both frame obesity as a chronic and pathological threat to health, at least to a point. The guideline texts frame obesity primarily as a sign of a behavioural problem, discrediting or ignoring many complicating sources of knowledge. In contrast, the 5As frames obesity as complex through diversifying the knowledge foundation embedded in the texts (e.g., including fat-related stigmatisation; health status differences among those classified as obese). Both de-emphasize social and environmental determinants of weight and health. CONCLUSION: Frames of problems used by brokers are not neutral, nor are decisions about how knowledge is excluded and included. Knowledge brokering, no matter how scientific and systematic, is limited by its frame. Recognizing the limits of each frame supports reflexivity in knowledge brokering and interventions taken to enhance health.
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 | 0.083 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".