Educational Inequalities: Difference and Diversity in Schools and Higher Education (2014) Kalwant Bhopal and Uvanney Maylor, editors
Bibliographic record
Abstract
Educational Inequalities: Difference and Diversity in Schools and Higher Education is a collection of essays by educational researchers from Europe, North America, and Australia.The editors feel that although there is considerable scholarly work on social equality and education, little recent work explores notions of difference and diversity in relation to race, class, and gender.Therefore, Kalwant Bhopal and Uvanney Maylor asked the contributors to investigate the ways in which dominant perspectives of difference, intersectionality, and institutional structures underpin and reinforce educational inequality in schools and institutions of higher education.In the introductory chapter, the editors state that this collection specifically examines areas of discrimination and disadvantage in education.They see the problems through the lenses of gender, race, and class but identify difficulties with application of such concepts to the study of students' experiences in education.They also seek to analyze contesting discourses of identity in different educational contexts.The collection is divided into three parts.Part 1, Difference, Diversity, and Inclusion, is a group of four essays.Through these, the editors expect readers to explore discourses of "difference" in educational contexts.Zeus Leonardo interrogates the status of whiteness in American education by exploring two significant camps, white reconstruction and white abolition.As forms of social practice, white reconstructionists offer discourses that transform white people into something other than an oppressive identity and ideology.White abolitionists perceive that it would not make any difference as long as white people distinguish themselves from others on the basis of their skin color.Accordingly, this chapter considers white reconstruction and white abolition for their conceptual and political values as they concern not only the revolution of whiteness but of race theory in general, particularly in relation to educational contexts.In the next essay, Gill Crozier assesses fairness in Britain using research in social justice and education.She identifies how injustices operate and manifest themselves in education, and concludes with an exploration of strategies for change to further equality of opportunity.In this essay, she reviews the existing research on educational underachievement among a cross section of black and other minority ethnic groups, especially black Caribbean, Bangladeshi, and Pakistani, and working class and middle class girls and boys to investigate the similarities and differences in their school experiences.Jasmine Rhamie then reviews the literature on the academic achievement of black pupils, focusing on research that identifies and promotes their academic success.She attempts to raise
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".