Bibliographic record
Abstract
The idea for this book came to me as I reflected on my several years of con ducting research in Winnipeg's inner city.It occurred to me that weresearchers, activists, community-based organizations, and local residents -have learned a great deal about doing research collaboratively and using it to advocate for change.After I spoke with colleagues about sharing our experiences, we decided to compile this collection of research stories.Our aim is quite simple: we hope that this book will be useful to anyone who is interested in conducting research as a means to social justice.Most of the chapters discuss past and current Manitoba Research Alliance (MRA) projects.The MRA is a consortium of academics, community researchers, and community-based organizations.It has been awarded three consecutive multi-year research grants through the Social Sciences and Humanities Research Council (SSHRC), beginning in 2003.The projects discussed in this book were funded through two of these grants, including a five-year Community University Research Alliance Grant (2007-11) and a seven-year Partnership Grant (2012-18).Although the MRA involves researchers from three Manitoba universities (University of Manitoba, University of Winnipeg, and the University College of the North), research grants awarded to the MRA are administered through a community-based non-profit research institute, the Canadian Centre for Policy Alternatives-Manitoba, which makes our approach somewhat unique.The centre is one of very few non-academic research institutes to receive SSHRC grants, and this has been key to our success.Administering the grant at the community level has been particularly conducive to the
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.371 | 0.200 |
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".