Learning by Dispossession: Objective Violence and Educational Failure
Bibliographic record
Abstract
Ellison Square and CHS are located several miles south of the landmark buildings and lakeshore attractions of Chicago’s downtown. The neighborhood and school are marked by concentrated poverty and ethnoracial segregation. They also contend with persistent issues related to violence. Indeed, when I first arrived in Ellison Square during the first week of September 2010 to begin this research, I found a school and community under emergency conditions because of two recent shootings involving students from CHS. One of these shootings took place on school grounds in full view of students, police, teachers, and parents, the other in a vacant lot near the school. Neither incident was fatal, both were gang related. Maya (African American freshman): I was in my class. I was in my division. I was right there because we were looking through the window. It was hot so we had opened the windows and we were looking out the window and we just saw the boy had just got shot and he was just lying there and somebody was like, “Get help.” And that’s when the teacher told us to sit down. All I saw was a car pulled over and the boy was just walking and they shot him. And that’s when the teacher was like, “Sit down, stop instigating” and stuff like that…I just felt hurt. Because it was like, it’s probably because of the gangs and the bad decisions he chose and stuff like that.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".