Designed to Fail: Media Representations of Racialized Classrooms and Schools
Bibliographic record
Abstract
Educational institutions are assumed to be racially neutral. However, media represents the achievement and ability of individual students and schools disparately and gives these attributes racial meaning. The scenes and sets in movies in the background seldom enter our consciousness and are assumed natural and normal in the context of movies and the stories they communicate. However, audiences, media institutions and set designers draw on shared cultural understandings to communicate and interpret the racial implications behind objects, placement of bodies, and scenery (Entman, 1993, pp. 52-53). Negative media portrayals of Black students and their school environments suggest that there is a problem with urban education. These representations and images suggest that the setting and the objects within it have purpose and meaning that is important in relaying the intended message. This study examines physical elements represented in classroom and school spaces in four movies: Akeelah and the Bee (2006), Finding Forrester (2000), Coach Carter (2005), High School Musical (2006). Utilizing a visual analysis of scenes depicting classrooms and school exteriors in these films, this study sought to examine how these representations of schools are presented as racialized spaces. Based on the data collected, the study concluded school spaces are represented disparately if they are assumed to contain Black racialized bodies than they are if they are assumed to be white spaces. Representations of urban schools with Black student populations contain multiple elements of surveillance, control and categorization.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".