Bibliographic record
Abstract
A young Black woman documents the systemic racism in her high school diary and calls for justice and educational reform. The prevalence of anti-Black racism and its many faces, from racial profiling to police brutality, in North America is indisputable. How do we stop racist ideas and violence if the very foundation of our society is built upon white supremacy? How do we end systemic racism if the majority do not experience it or question its existence? Do our schools instill children with the ideals of equality and tolerance, or do they reinforce differences and teach children of colour that they don’t belong? # BlackInSchool is Habiba Cooper Diallo’s high school journal, in which she documents, processes, and resists the systemic racism, microaggressions, stereotypes, and outright racism she experienced in Canada’s education system. Powerful and eye-opening, Cooper Diallo illustrates how our schools reinforce rather than erode racism: the handcuffing and frisking of students of colour by police at school; one-dimensional, tokenistic curricula portraying Black people; and the constant barrage of overt racism from students and staff alike. She shows how systemic racism works, how it alienates and seeks to destroys a child’s sense of self. She shows how our institutions work to erase the lived experiences of Black youth and try to erase Black youth themselves. Cooper Diallo’s words will resonate with some, but should shock, appall, and animate a great many more into action towards a society that is truly equitable for all.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.651 | 0.352 |
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".