Editors' Note / Note des rédacteur(ice)s
Bibliographic record
Abstract
A History of Racism in Canada" eloquently crafts the argument that the effects of institutionalized racism that has spanned several centuries, can still be felt by Afro-Canadians participating in the labour market today. Following Bakan's assertion that racist and hegemonic practices are heavily embedded in Canada's political structure, Kihika chronologically traces the consequences of neo-liberal policies, asserting that these policies, on the surface, appear to support the ideals of multiculturalism and inclusivity, but in reality, have only served to further propagate the capitalist and imperialist agendas of those in power. As a result, it is evidenced through official government statistics and past research, that Afro-Canadians continue to struggle economically, and are positioned within the lower rung of the, as Kihika notes, labor market hierarchy. With little earning power and few opportunities for stable employment, the author suggests that this particular racialized group continues to suffer the illconsequences of age old structures of systemic oppression.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.030 | 0.015 |
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".