1 Community University Research Partnerships- A Critical Reflection and an Alternative Experience
Bibliographic record
Abstract
In recent years community-university partnerships have become the ‘flavour of the month’. There are pressures from funding bodies such as SSHRC on the academic side and Social Development Canada (HRDC or whatever its current incarnation is) on the non-profit side to build partnerships on research projects. In this discussion, I will ask how can research partnerships be built on principles of equality and mutual interest, in which each group benefits. More important for me is to ask how can struggles for social and economic justice be furthered by these relationships? The discussion will begin with some contextual and wider social questions, examine aspects of the SSHRC CURA program and conclude with a practice example of the collaboration between the Immigrant Workers Centre and the researchers involved in this project over the past 4 years and more recently with Solidarity Across Borders. Lessons from this experience will be shared. First, what can be gained by community organizations and what can be gained by university researchers in research partnerships? For community organizations participation in research projects can bring several important benefits. Research can help with building and deepening a social and political analysis that can be used to strengthen intervention. Projects can help the organization’s members or staff build skills in research, and interviewing. It can be used as a means of recruitment as people interviewed
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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 | 0.070 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.050 | 0.064 |
| Scholarly communication | 0.042 | 0.033 |
| Open science | 0.007 | 0.040 |
| Research integrity | 0.022 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 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".