“JUST CHECKING”: RACIAL PROFILING AND THE CRIMINALIZATION OF BLACK STUDENTS AT POST-SECONDARY INSTITUTIONS
Bibliographic record
Abstract
“Just Checking”: Racial Profiling and the Criminalization of Black Students at Post-Secondary Institutions This dissertation examined the experiences of Black students who came into contact with campus security at five universities and one college in Ontario, Canada. Critical race theory and methodology, and qualitative research design amplified the voices of 17 participants who shared stories of racial profiling marked by racialization, criminalization, surveillance, and the normalized scrutiny of Black life. These experiences are legitimized by post-secondary institutions that are actively reproducing the very racism they are often celebrated for interrogating by allowing a culture of anti-Black racism to exist with impunity. Importantly, this dissertation shares stories of resistance and resilience as Black students are showcasing their culture and identity irrespective of profiling as an anticipated reality and consequence. They will not be silenced or pushed out of spaces they claim. Theoretically, this dissertation addresses the convergence of racial neoliberalism, carceral logic, race, and space which continue to be emerging areas of scholarship in social work. It complements scholarship, policy, and practice on racial profiling in Canada, education, and Black excellence and concludes with recommendations to address anti-Black racism in postsecondary settings and ameliorate social work pedagogy in this area.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.021 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".