Community Building & Racial Justice — with Lama Mugabo
Bibliographic record
Abstract
“What I see that's hopeful coming out of this pandemic is that I think we've revitalized our ability to work in solidarity.”\n \nLama Mugabo joins Below the Radar to speak to building community and solidarity, from Rwanda to Hogan’s Alley. Lama is a Rwandan-born community organizer and planner with deep roots in the Downtown Eastside and the Black community in Vancouver.\n \nIn this episode, Lama joins host Am Johal to speak to his work around reconstruction and community building Rwanda, following the genocide of 1994. A co-founder of Building Bridges with Rwanda, Lama talks about fostering awareness and international solidarity with Rwandans, Canadians, and the diaspora community.\n \nHaving worked for decades in the Downtown Eastside community, Lama draws connections between his work internationally and locally. He shares his experiences of engaging community with Hogan’s Alley Society around housing, discriminatory street checks, and rebuilding the once-thriving Black community that was displaced for the construction of the viaducts. Lama also speaks to how the COVID-19 pandemic has underlined the importance of the human right to housing, a need for increased welfare rates, and how growing food in community promotes health and connection.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.022 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.010 |
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".