The Education of African Canadian Children: Critical Perspectives by Ibrahim, A. & Abdi, A. (Eds.)
Bibliographic record
Abstract
PositionalityPrior to migrating, I was born and raised in Jamaica and identify as an African-Caribbean, educated, immigrant woman.In Canada, my dark skin and lived experiences have included discrimination, and social marginalization which have shaped the lens through which I view the world.It is a lens that highlights and focuses on power relations, prejudice, racism, hope, agency, justice, and acts of resistance.A teacher by training, I completed an undergraduate degree in Jamaica, before moving to Canada to pursue graduate studies.I had very positive schooling and university experiences in Jamaica.My teachers demanded nothing but the best from me.I was reminded by family, friends, and my teachers that I was intelligent and could achieve my highest dreams.Further, I had the privilege of being educated in a country where I did not feel either minoritized or racialized.All around me, I could see persons who looked and sounded like me at all levels of society, whether as market women, teachers, doctors, professors, entrepreneurs, or as the head of government.This allowed me to have a strong self-esteem and the confidence that I could be successful if I worked hard enough.School was challenging but in many ways it was also nurturing, I never felt alienated, incapable, or inferior.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".