Bibliographic record
Abstract
This article details the role that networks play in the creation and implementation of a comprehensive knowledge mobilization strategy. Using the activities of the Canadian Homelessness Research Network (CHRN) as a case study, the authors draw on in-depth interviews, participant observation, and document analysis to understand how the interactivity cultivated in a multi-stakeholder partnership can increase the impact of research on policy, practice, public opinion, and, in this case, the lived experiences of people who are homeless. The article details the diverse activities of the CHRN (e.g., its methodologies, processes, and tools), highlighting the tension points, successes, and failures of particular approaches. Findings bring into view a) the CHRN’s role as a central connecting node, linking multiple and diverse individuals, institutions, and other networks; b) relations of reciprocity, which support ongoing interactivity between network members; and c) the changes (e.g., in research use) that network activities have influenced. Data suggest that the use of research evidence to co-produce “useable content” is a key indicator of network productivity.
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 | 0.035 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.006 | 0.042 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".