CASE 4: Journeying Together—Unlearning is the New Learning
Bibliographic record
Abstract
Immigration plays an integral part in the development of multiculturalism within Canada, with the majority of immigrants representing visible minority communities. Research shows Canada’s newest settlers are more likely to experience health disparities and inequalities compared to non-indigenous and Canadian-born residents. Research further indicates that the access and quality of health services is commonly compromised when health care and/or service providers do not respond appropriately to language and cultural factors impacting newcomer health. Communities vary in culture, traditions, and language, however, are often grouped together. One approach will not fit all with the cultural and ethnic differences within these communities. Ellie is faced with the challenging task to develop a project curriculum that promotes newcomers’ sense of belonging to the community. This teaching case highlights the importance of intersectionality, addresses stigma, and discusses the need for providers to apply antioppressive and antiracist practices when working with diverse communities. The case introduces strategies that can be employed as living processes by which newcomers may contribute as active stakeholders to the overall culture of learning and community well-being.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".