What early childhood educators need to know about fostering Black children's positive identification with Blackness: Foregrounding mothers' perspectives
Bibliographic record
Abstract
[Introduction]: "Approximately one hundred and thirty years after the abolition of slavery in Canada, Canada's immigration policy in the late 1960s 'welcomed' Blacks from the African diaspora who were brought in as cheap labourers, working in low paying unskilled dead-end jobs, which were and remain racially and gender segmented (Brickner & Straehle, 2010; Nelson, 2010). Today whether they make up 45% of the Black population born in Canada (Statistics Canada, 20202) or are immigrants or refugees, people of African descent are overrepresented in both the judicial and prison systems, as well as in child protective services, where Black children are disproportionately taken away from their families and placed in the care of the state. Black students, along with Indigenous students, are also more likely to be expelled, or pushed out, from schools in comparison to other racial groups in and around the city of Toronto (James & Turner, 2017); a city deemed to be the most multicultural city in the world by the BBC in 2016. The history of Blacks in Canada involves oppression and exploitation, as Blacks were and are still marginalised and continue to occupy a subordinate role in Canadian society."
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".