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Record W3139889532 · doi:10.1109/mahc.2021.3055417

From Papers to Programs: Courts, Corporations, Clinics, and the Battle Over Computerized Psychological Testing

2021· article· en· W3139889532 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Annals of the History of Computing · 2021
Typearticle
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsUniversity of Toronto
FundersAssociation for Computing Machinery
KeywordsBattlePersonalityScholarshipInterpretation (philosophy)PoliticsHistory of computingMediationTest (biology)SoftwareSoftware developmentPsychological testingPublic relationsComputer scienceLawPsychologySocial psychologyPolitical scienceAlgorithm

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.121
GPT teacher head0.322
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it