Building a Policy-Oriented Research Partnership for Knowledge Mobilization and Knowledge Transfer: The Case of the Canadian Metropolis Project
Bibliographic record
Abstract
The aim of this paper is to examine government–university–community partnerships for knowledge mobilization (KM) and knowledge transfer (KT) in the area of immigration and settlement research using the illustrative case of the Canadian Metropolis Project. The Metropolis Project in Canada began in 1995 with the goal of enhancing policy-oriented research capacity for immigration and settlement and developing ways to better use this research in government decision-making. Core funding for this partnership was provided jointly by Citizenship Immigration Canada (CIC), a department of the Government of Canada and the primary social science granting agency, the Social Science and Humanities Research Council (SSHRC). As of 2012, and subsequent to three successful funding phases, the decision was made to end government and SSHRC core funding for this initiative, however, other non-governmental funding avenues are being explored. The longevity of this partnership and the conclusion of this specific initiative present an opportunity to reflect critically on the nature of such partnerships. This paper is an attempt to identify some of the key themes, issues and challenges related to research partnerships, KM and KT. Also, with the aid of an illustrative case, it aims to specify some of the possibilities and limitations of this kind of policy relevant knowledge mobilization. Special consideration will be placed on the context in which the demand for knowledge mobilization and knowledge transfer has emerged. This examination has considerable international relevance as the Canadian Metropolis Project offers the leading example of a research partnership in the field of immigration and settlement.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.073 | 0.034 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".