Connecting the dots: Understanding the flow of research knowledge within a research brokering network
Bibliographic record
Abstract
Networks are frequently cited as an important knowledge mobilization strategy; however, there is little empirical research that considers how they connect research and practice. Taking a social network perspective, I explore how central office personnel find, understand and share research knowledge within a research brokering network. This mixed methods case study focused on the first two cohorts of school district Mental Health Leaders participating Ontario’s Child and Youth Mental Health program (N=37). Data were collected and analyzed in two phases: 1) the administration of a social network survey to all participants (response rate = 97%), and 2) follow-up interviews with key informants identified by the social network analysis (N=11). The findings indicate that this is a sparse network and the pattern of incoming ties tends to focus on a subset of individuals. When the identified key players (who are sometimes but not always program staff) are removed, network activity is cut by more than half; the removal of the remaining program staff members renders the network virtually non-existent. Research knowledge typically flowed in a single direction as there were few reciprocal ties within the network. Interview data yielded some important insights indicating that participants perceived formal CYMH events as their main access points to research knowledge and that Mental Health Leaders who were identified as prominent sources of research knowledge had pre-existing relationships with CYMH program staff.
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 | 0.038 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.019 | 0.045 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.003 |
| 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".