Who’s indoctrinating whom?: searching for anti-racist ideology in educational policy since 2020
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it