Shifting Perceptions: Transforming anti-racism praxis into prototypes
Bibliographic record
Abstract
The Edmonton Shift Lab is a unique social innovation lab from Edmonton, Alberta, Canada, that is doing ground-breaking antiracism work through a combination of systems thinking, design thinking and Indigenous epistemologies. For the last five years, the Shift Lab has worked with volunteer community members from the human services sector in Edmonton to explore anti-racism approaches that could be impactful and scalable. Fighting racism requires a systemic approach and keen insights into behavioural science. Our findings challenge the established diversity, equity, and inclusion workshop approach and improve upon it through the creation of tangible, actionable prototypes that are being adopted by community partners. These prototypes include a subscription box service, a public safety brochure, a tabletop board game, an app and an educational curriculum for landlords. Each shows promise in making systemic change to the attitudes and actions that contribute to racist behaviours. We have begun collecting and tabulating feedback about what works and what doesn’t, and we are eager to share these observations in the context of systemic design.
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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".