Bibliographic record
Abstract
"Power, Race, and Higher Education is a parallel narrative written by two scholars. Kakali Bhattacharya, who is a South Asian woman who immigrated to the United States to pursue her graduate degrees and eventually became an academic. Kent Gillen is a White man who focuses on completing his doctoral studies under Kakaliâs supervision. Kent comes to a crossroad where he has to interrogate his sociocultural position, how he benefits from a White supremacist system, even if he did not ask for any of the benefits or had his personal plights. Embedded in the dilemmas are implications for cross-cultural qualitative research, understanding of how whiteness functions, and how we attend to our deepest wounds as we work to become allies and build bridges. This book can be used in undergraduate and graduate courses in race and culture studies in the social sciences and humanities, qualitative methods courses, and graduate classes that help students with writing up qualitative research. Individual graduate students and professors who advise graduate students may benefit from this text. âRiveting, courageous, innovative and brave! This spell-binding book not only holds your attention, it holds you to account as you read a beautifully integrated narrative that weaves theory, research, artistry and practice into an utterly compelling positioning of our power relations within society and the academy.â Rita Irwin, Ph.D., Professor of Art Education in the Department of Curriculum and Pedagogy, and Associate Dean of Teacher Education, at the University of British Columbia in Vancouver âIt is a book that will inform scholarly conversations with both undergraduate and graduate students, and influence future qualitative researchers.â Enrique Alemán, Jr., Ph.D., Professor & Chair, Educational Leadership & Policy Studies, University of Texas at San Antonio âTold in honest and straightforward language, this engaging book has much to say about scholarly responsibility, White privilege, and our necessary reconciliation toward equity and a deep awareness of self.â Johnny Saldaña, Professor Emeritus, Arizona State University Kakali Bhattacharya is an associate professor at the Kansas State University in Manhattan, Kansas. Norman K. Gillen is an adjunct instructor, who teaches English and Industrial Communications at Del Mar College.".
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".