Documenting knowledge mobilization: a quantitative analysis of citation and reported speech in a Canadian public inquiry
Bibliographic record
Abstract
Abstract Research into citation and reported speech has identified a number of functions, such as measures of influence, solidarity and distancing, demonstration, and construction of knowledge. This study brings citation analysis to knowledge mobilization – a situation in which research informs public policy. In the present case, it was a Canadian public inquiry on a wrongful murder conviction that prompted many police departments across the country to adopt new procedures that were informed by psychology research to minimize the chances of wrongful conviction. This article focuses on the result of a quantitative analysis that goes beyond simple counting to provide a citation profile of the inquiry report and discusses what such systematic description can reveal. The findings include a particular attribution practice of privileging expert statements but only when they are attributed to the speakers rather than to their writing or to the transcripts of their speech. In addition, the quantitative data revealed no correlation between rhetorical framing and formal citation or direct quotes. These findings lead to discussions on functions of citation and reported speech in this document, as well as the relationship between linguistic form and knowledge mobilization.
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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 | 0.024 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".