Moving the Needle on Poverty
Bibliographic record
Abstract
Contents: \nPreface - Pathways to Poverty Reduction through Community-Campus Partnerships \nChapter One: Creating Strategic Partnerships to Influence Policy (Liz Weaver) \nChapter Two: Models of Community-Campus Engagement in the Poverty Reduction Hub of CFICE (Karen Schwartz) \nChapter Three: University and Community Collaboration: Achieving Social Change (Erin Bigney, Tracey Chiasson, Melanie Hientz, Robert MacKinnon and Cathy Wright) \nChapter Four: On a Path of True Reconciliation: Investing in a Poverty-free Saskatoon (Colleen Christopherson-Côté, Lisa Erickson, Isobel M. Findlay and Vanessa Charles) \nChapter Five: Using Campus Community Engagement to Build Capacity for Poverty Reduction (Amanda Lefrancois) \nChapter Six: Shifting Societal Attitudes Regarding Poverty: Reflections on a Successful Community-University Partnership ( \nMary MacKeigan, Jessica Wiese, Terry Mitchell, Colleen Loomis and Alexa Stovold) \nChapter Seven: Models of Collaboration: Does Community Engagement with University Colleges Have an Impact on Poverty Reduction? (Polly Leonard and Karen Schwartz) \nChapter Eight: A Peephole into the Student Experience: Student Research Assistants on their Experiences in the Poverty Reduction Hub (Aaron Kozak, Zhaocheng Zeng and Natasha Pei) \nChapter Nine: Poverty Reduction Hub Evaluation (Aaron Kozak, Karen Schwartz, Amanda Lefrancois and Liz Weaver) \nChapter Ten: Conclusion (Magdalene Goemans)
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.057 | 0.011 |
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".