Moving the Needle on Poverty
Bibliographic record
Abstract
<small>Contents:</br> \nPreface - Pathways to Poverty Reduction through Community-Campus Partnerships</br> \nChapter One: Creating Strategic Partnerships to Influence Policy (Liz Weaver)</br> \nChapter Two: Models of Community-Campus Engagement in the Poverty Reduction Hub of CFICE (Karen Schwartz)</br> \nChapter Three: University and Community Collaboration: Achieving Social Change (Erin Bigney, Tracey Chiasson, Melanie Hientz, Robert MacKinnon and Cathy Wright)</br> \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)</br> \nChapter Five: Using Campus Community Engagement to Build Capacity for Poverty Reduction (Amanda Lefrancois)</br> \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)</br> \nChapter Seven: Models of Collaboration: Does Community Engagement with University Colleges Have an Impact on Poverty Reduction? (Polly Leonard and Karen Schwartz)</br> \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) </br> \nChapter Nine: Poverty Reduction Hub Evaluation (Aaron Kozak, Karen Schwartz, Amanda Lefrancois and Liz Weaver)</br> \nChapter Ten: Conclusion (Magdalene Goemans)</small>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".