Bibliographic record
Abstract
Abstract The use of psychological concepts and data to promote ideas of an enduring racial hierarchy dates from the late 1800s and has continued to the present. The history of scientific racism in psychology is intertwined with broader debates, anxieties, and political issues in American society. With the rise of intelligence testing, joined with ideas of eugenic progress and dysgenic reproduction, psychological concepts and data came to play an important role in naturalizing racial inequality. Although racial comparisons were not the primary concern of most early mental testing, results were employed to justify beliefs regarding Black “educability” and the dangers of Southern and Eastern European immigration. Mainstream American psychology became increasingly liberal and anti-racist in the late 1930s and after World War II. However, scientific racism did not disappear and underwent renewal during the civil rights era and again during the 1970s and 1990s, Intelligence test scores were a primary weapon in attempts to preserve segregated schools and later to justify economic inequality. In the case of Henry Garrett, Arthur Jensen, and Philippe Rushton, their work included active, public promotion of their ideas of enduring racial differences, and involvement with publications and groups under control of racial extremists and neo-Nazis. Despite 100 years of strong critiques of scientific racism, a small but active group of psychologists helped revive vicious 19th-century claims regarding Black intelligence, brain size, morality, criminality, and sexuality, presented as detached scientific facts. These new claims were used in popular campaigns that aimed to eliminate government programs, promote racial separation, and increase immigration restriction. This troubling history raises important ethical questions for the discipline.
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 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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.030 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".