Reading, writing, and racialization: The social construction of Blackness for students and educators in a Prince George's County public middle school
Bibliographic record
Abstract
We do not fully understand how people of African descent, both in the United States and foreign born, conceptualize their integration into the predominantly "Black space" of Prince George's County or if and how the constituents of Black spaces are conceived of as diverse. Furthermore, we do not know enough about how these processes operate in the public school setting. This dissertation focuses on interrogating the public institution of Prince George's County Public Schools to examine how students and educators construct, negotiate, challenge, and reproduce notions of Blackness. The first research question is "how do youth of African descent, including the U.S. born children of immigrants and those with a Spanish ethno-linguistic heritage, construct or deconstruct a Black identity in a United States context"? The second large area of inquiry asks "how are educators influencing social constructions of Blackness"? There is also a focus on if and how the educational process acknowledges and responds to complex dynamics among students, how they identify, and how they get identified racially, ethnically, and culturally by others. I investigate this quandary by using ethnographic data conducted over a seventeen month span in a middle school. I find that all people in the school, with an intentional focus on students of African descent with a Spanish ethno-linguistic heritage are engaged in their own dialogues and complex constructions of what it means to be Black.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.020 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".