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Record W4249006331 · doi:10.1037/e681532011-001

Reading, Writing, and Racialization: The Social Construction of Blackness for Students and Educators in a Prince George's County Public Middle School

2010· dataset· en· W4249006331 on OpenAlexaboutno aff
Arvenita Washington

Bibliographic record

VenuePsycEXTRA Dataset · 2010
Typedataset
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsRacializationGeorge (robot)Reading (process)SociologyPublic housingMedia studiesGender studiesArtArt historyPolitical scienceRace (biology)Law

Abstract

fetched live from OpenAlex

<p>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.</p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.424
Teacher spread0.374 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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