“Inequalities of Children in Original Endowment”: How Intelligence Testing Transformed Early Special Education in a North American City School System
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
“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.”
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 it