Bibliographic record
Abstract
Don't look away: Embracing anti-bias classrooms is a resource that can serve current practitioners in early childhood education (ECE) to expand on their knowledge of antibias education, anti-racism, culturally responsive teaching with tools that can enhance their practice of culturally responsive, anti-bias pedagogies.Practitioners that strive to confront their unconscious biases and seek to teach more equitably would benefit from engaging with this book either as a personal exercise or with their community of practice.The book consists of eight chapters that explore and explain the historical, societal, and cultural context that make anti-bias education a necessary part of an ECE's practice.A ninth chapter is dedicated to the references and recommended readings in which the authors discuss the seminal research they draw from.Overall, in each chapter the authors encourage the reader to confront their own beliefs regarding race, bias, and equity while recognizing the impact they have in their classrooms to mitigate the harms caused by negative biases to young children's development and learning.In their introduction, the authors primarily drawing on the history of children's education and policy in the United States to lay the foundation of work that led anti-bias education.They begin by confronting implicit bias and its significant impacts on Black children's education, e.g. the higher-than-average suspension rates of Black children when compared to their White peers.Next, they speak to the conception and
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".