Who’s indoctrinating whom?: searching for anti-racist ideology in educational policy since 2020
Bibliographic record
Abstract
Amid debates about CRT in education, this paper critically analyses laws that have reportedly sought to expand ‘education on racism, bias, the contributions of specific racial or ethnic groups to U.S. history, or related topics’ with the hypothesis that there would be little evidence of anti-racist ideology in policies pertaining to curriculum. The research design thus leans on King and Chandler’s (2016) distinction between non-racist and antiracist stances, as well as Andreotti et al’.s (2015) social cartography that maps out ‘soft-reform’ and ‘radical reform’ spaces, to achieve a latent content analysis of 14 pieces of legislation across 13 states since 2020 to identify and analyse the ideological characteristics of these pieces of legislation. Only four of the 14 documents from four different states contain a significant anti-racist ideological leaning; the others express a liberal multicultural ideological position that celebrates difference and recognizes contributions, but does not examine systemic racism. Thus, among states that are legislating more ethnic studies, the vast majority do not legislate anti-racist positions. This paper concludes that there is little evidence of anti-racist ideas being legislated into primary and secondary education in the United States, and that most curricular reforms toe a non-critical ideological line.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".