“Inequalities of Children in Original Endowment”: How Intelligence Testing Transformed Early Special Education in a North American City School System
Bibliographic record
Abstract
“There are few if any more significant events in modern educational history than the developments which have recently taken place in methods of mental measurement,” Lewis Terman wrote in 1923 about the intelligence testing movement he did so much to pioneer in American schools throughout the 1920s. Indeed educational historians, particularly Paul Chapman, have shown that the rise of intelligence testing provoked large and relatively swift changes in public education, enabling school systems to sort and stream their students by ability on an unprecedented scale. “By 1930,” Chapman writes, “both intelligence testing and ability grouping had become central features of the educational system.” Less often talked about are the effects of intelligence testing and the concept of intelligence quotient (IQ) on early special education classes, and on the pupils who attended them. In fact, Terman recognized the significance of IQ testing to special education as well. In 1919, he wrote that IQ tests would help to turn the existing logic of learning problems on its head by proving that “the retardation problem is exactly the reverse of what it is popularly supposed to be.”
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".