The House that Hate Built: Fixing the Mess We Made
Bibliographic record
Abstract
ABSTRACT The long-standing entrenchment of racism as a societal norm, along with its pervasive influence on large-scale assessment systems, continues to perpetuate racial and ethnic injustice globally. White supremacist logics underpin systemic oppression across institutions in North America (e.g., Canada & the United States), South America (e.g., Brazil), and Europe, manifesting in criminal justice systems, social services, and education settings. This manuscript highlights how assessment tools, shaped by racist logics, contribute to the marginalization and dehumanization of racially and ethnically minoritized populations. I describe how psychometric methods often perpetuate oppressive ideologies, resulting in assessments that function as discriminatory practices under the guise of objective measures of merit. I also propose a path forward aimed at disrupting these logics. This manuscript presents a justice-oriented approach to assessment design that is unapologetically antiracist and aims to disrupt the historical legacy of white supremacist and racist logics (rooted in hate) within the fields of assessment and measurement. This approach requires (a) collective responsibility for pursuing justice, regardless of our role in the system; (b) the establishment of a monitoring and evaluation system; (c) investment in developing the critical consciousness of the entire assessment/measurement field; (d) transparency; (e) respect for the cultural norms of the world’s majority; and (f) a commitment to seeking evidence of justice in our measures, rather than simply removing bias. I advocate for an overarching ethos of love (justice is love) to actively correct the harm inflicted on racially and ethnically minoritized populations and reframe assessments as tools for liberation.
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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 | 0.026 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.066 |
| Scholarly communication | 0.025 | 0.029 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".