From Papers to Programs: Courts, Corporations, Clinics, and the Battle Over Computerized Psychological Testing
Bibliographic record
Abstract
This article examines the role of technology firms in computerizing psychological tests from the 1960s to 1980s. It focuses on National Computer Systems (NCS)'s development of computer software to interpret the Minnesota Multiphasic Personality Inventory. NCS trumpeted their computerized interpretation as a way to free up clerical labor and mitigate human bias, even as psychologists cautioned that proprietary algorithms risked obscuring decision rules. Clinics, courtrooms, and businesses all had competing interests in the use of computerized personality tests. I argue that test developers promoted computerized psychological tests as technical fixes for bias, even as courts and psychologists pointed to the complex layers of technological and social mediation embedded in software programs for psychological tests. This article contributes to histories of computing emphasizing the importance of intellectual property law in software development; to the relationship between labor, technology, and expertise; and to scholarship on the history and politics of algorithms.
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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 | 0.021 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.027 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".