Bibliographic record
Abstract
David J. Luke’s Affirmative Action and Black Student Success is a concrete and comprehensive exploration into diversity programs on college campuses and their impact on Black student success and outcomes. Viewed over the span of 12 years, three large, public universities in the United States and Canada provide dynamic settings for this book’s comparative focus on diversity initiatives. The author identifies key regional and national differences between these settings, as well as differences in the way diversity is framed and understood to illustrate how diversity programs and policies are shaped and the extent and ways in which these programs and policies then shape student experiences and outcomes. The values and meanings organizations ascribe to diversity, inclusion, and equity are frequently in transition, and the book’s compelling analysis conveys the importance of race in these contexts—when racism is presumed to be in decline, as is the case in colorblindness and demonstrations of multiculturalist ideals, racial inequalities are concealed and remain unnoticed. The author makes a range of practical recommendations and argues that clear and explicit goals about race and representation are integral in the expansion and preservation of inclusive institutional environments. Unflinching in its critique and pragmatic with its recommendations, this book offers invaluable analysis for university leaders, diversity officers, and student affairs professionals, as much as it provides new insights for scholars and educators of racism, higher education, diversity, and organizational culture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".