TACKLING POVERTY THROUGH HOLISTIC, INTERCONNECTED, NEIGHBOURHOOD-BASED INTERGENERATIONAL LEARNING: THE CASE OF WINNIPEGâS SELKIRK AVENUE
Bibliographic record
Abstract
Winnipeg is the capital city in the Province of Manitoba, Canada. It is home to a high proportion of Aboriginal people, many of whom live far below the poverty line and drop out of school at an early age. Many have lived in poverty for generations and have little hope of escaping it. The reasons are in part attributable to colonial policies that have left a legacy of despair and distrust, particularly in the education system. \n \nCommunity-based organizations, post-secondary education institutions, governments and others are working in collaboration to build a holistic education model that provides opportunities for Aboriginal people and other multi-barriered residents through a cradle to college approach. Programs recognize the damaging effects of colonization and integrate decolonizing pedagogical methods. Recently the community acquired a century old property, the Merchants Hotel, which had become a magnet for violence and many serious social problems. The community’s vision is to reclaim this space as a multi-faceted place of learning that will further connect and expand upon existing educational initiatives. This paper and video describe the historical context, our pedagogical approach and what we have learned to date as we move forward with the development of an intergenerational community campus.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".