Bibliographic record
Abstract
In August 2022, Garvin Yapp, a 57-year-old migrant farm worker from Jamaica, was killed while working on a tobacco farm in Ontario, Canada. Yapp’s untimely and preventable death came just days after Jamaican farm workers penned a letter comparing their working conditions in Southern Ontario to “systematic slavery.” What was glaringly missing were accounts of the experiences of Black immigrants, like Yapp or my grandmother, who represent a large percentage of Black Canadians. Their stories and our stories were missing. When in reality, “We deh yah!” ). Black immigrants, specifically those from the eastern Caribbean, are a notable part of Canada’s history and present yet the Canadian curriculum often essentializes the Black American experience as representative of Black Canadians. While Black Canadians born in the US are an important part of the Black Canadian population, this essentialization of Black Canadians obscures the lived realities of Black Canadians who often experience antiblackness that is shaped by their intersectional identity, related to citizenship, language, and socioeconomic status. Thus, to truly apprehend and challenge the manifestation of antiblackness in Canada, it is imperative to recognize and understand the diversity of Black Canadians. This article offers two things educational stakeholders, like teachers, should consider in order to work towards recognizing the diversity of Black Canada
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".