Knowledge Mobilization as Design: The Case of the Canadian Homelessness Research Network
Bibliographic record
Abstract
The Canadian Homelessness Research Network (CHRN) was established to create a stronger link between homelessness research, policy, and practice. The knowledge mobilization strategy of the CHRN encompasses engaged scholarship, networking, and innovative dissemination practices. Part of the learning of the CHRN has been the need to reimagine and redefine the traditional relationship between content creation and dissemination. Using a “design thinking” perspective, the CHRN has nurtured a practice that integrates collaborative processes of knowledge and content development with a more assertive involvement in different aspects of publishing (and modes of publication), including graphic design, marketing, communications, and dissemination, with the goal of increasing the impact of research. This article explores this shift, using examples of work the CHRN produced and disseminated through the Homeless Hub.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.081 | 0.055 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".