Meaning-making around ethnicity-race and identity among students of color in a historically White middle school
Bibliographic record
Abstract
Efforts to regulate the teaching of ethnicity-race and other “divisive concepts” in schools pose a threat to the affirmation and well-being of students with racially marginalized identities. Guided by phenomenological variant of ecological systems theory (PVEST), we examined the salience of ethnicity-race and ethnic-racial identity for five students of color from different ethnic-racial groups in a historically White middle school. Group experiential themes generated through interpretive phenomenological analysis (IPA) revealed continuity and variability in students’ meaning-making accounts. Students recognized ERI as a potent dimension of self, and school ethnic-racial diversity as a source of learning and peer connection, yet the salience of each varied according to ethnic-racial positionality. Ethnicity-race was named as a source of differential treatment within the peer group and curriculum, yet students’ strategies for coping were not uniform. Implications for teachers who aim to stand in solidarity with youth in challenging hegemonic Whiteness in schools are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".