A Comparative Case Study Of Black Student Responses To Diversity-Related Media At One U.S. And One Canadian University
Bibliographic record
Abstract
This doctoral dissertation is a qualitative comparative case study about Black student responses to diversity-related print and web material at one Canadian and one American university. The research methodologies used in this study included: 1) a critical discourse analysis of diversity-related media and policy documents and 2) a thematic narrative analysis of in-depth interviews. A cross-sectional sample of forty-two students were selected for in-depth interviews between both universities. A sample of twelve Diversity administrators were also interviewed for the study. The central research question asked how Black students articulated their experiences in response to diversity-related media within their universities. This study drew on several overlapping theoretical frameworks. These theoretical frameworks included color-blind racism (Bonilla-Silva, 2014), critical discourse analysis (Fairclough, 1995), institutional diversity and language (Ahmed, 2012), and lastly I considered the agency of the students as demonstrated through their use of counter-hegemonic discourses. The main findings from the textual and visual analysis of media was that both universities conceptualized diversity as differences in visible social identities. The institutional emphasis was on the diversity of the student population and managerial diversity discourses were fully integrated in brochures, web sites, and policy statements. Students in Canada and the U.S. generally reported similar experiences. Most students observed contradictions between their experiences and the institutional diversity discourses. Diversity administrators felt silenced and disillusioned by the lack of change that they were able to effect. This study connects diversity policies and practices in higher education. The findings also illuminate some of the tensions and current challenges about race and higher education.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.059 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".