Contemporary issues in the sociology of race and ethnicity : a critical reader
Bibliographic record
Abstract
Contents: George J. Sefa Dei/Meredith Lordan: Introduction - George J. Sefa Dei: Reframing Critical Anti-Racist Theory (CART) for Contemporary Times - Meredith Lordan: The Race to Self: Critical Anti-Racism Theory in a Global Change Era - heory in a Global Change Era - Lessons from Rio+20 - the United Nations Conference on Sustainable Development - Paul Banahene Adjei: When Blackness Shows Up Uninvited: Examining the Murder of Trayvon Martin through Fanonian Racial Interpellation - Philip S. S. Howard: The Smack of Self-Determination: A Fanonian Analysis of the Africentric Schooling Debate in Toronto - Shaun Chen: Schooling, Interrupted: What France's Last Sociologist Might Have Said about Canada's First Black-focused School - Brandy Jensen: Race Erased? Arizona's Ban on Ethnic Studies - Preeia Surajbali: Situating My Standpoint: My Relationship to Black Feminist Thought as an Indo-Caribbean Canadian Woman of Color - Jozef Konyari: Understanding the Pathology and Cure for Euro-Colonial Whiteness: A Psychological, Behavioral, and Systemic Analysis - Mairi McDermott/Marlon Simmons: Embodiment and the Spatialization of Race - Kathleen Conroy: Black Males and Exclusionary Schooling Practices: 'Common-Sense' Racism and the Need for a Critical Anti-Racist Approach - George J. Sefa Dei/Meredith Lordan: Conclusion: Where Does Critical Anti-Racism Theory Lead Us? Considering Educational, Policy, and Community Implications.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".