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Record W2759069522 · doi:10.1177/0952695117722717

Numbering the mind

2017· article· en· W2759069522 on OpenAlexfundno aff
Jacy L. Young

Bibliographic record

VenueHistory of the Human Sciences · 2017
Typearticle
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsThurstone scaleLikert scalePsychologyCraftPsychological researchSubject (documents)Scale (ratio)Social psychologyApplied psychologyComputer science

Abstract

fetched live from OpenAlex

During the interwar years psychologists Louis Leon Thurstone and Rensis Likert produced newly standardized forms of questionnaires. Both built on developments in mental testing, including the use of restricted sets of answers and the emergence of statistical techniques, to create questionnaires that employed numerical scaling. This transformation in shape of questionnaires was intimately tied up with both psychologists’ nominal subject of investigation: attitudes. Efforts to render psychology a socially valuable and influential science spurred psychologists to create sophisticated and increasingly precise means of measuring social attitudes. Reducing mental dispositions to mere numbers on a scale, these developments also initiated new relationships between psychology and the public. Rather than engage a wide spectrum of the public directly in the research process, questionnaire research was limited to those within academic circles. Even so, research with questionnaires aimed to comment on attitudes in the public more broadly. The kind of ‘thin description’ afforded by numerical scales, though used to measure individual psychological subjects, afforded psychologists the opportunity to craft their vision of an increasingly attitudinal public, one positioned as best governed with the aid of psychological expertise.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.634
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.007
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.153
GPT teacher head0.413
Teacher spread0.259 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2017
Admission routes1
Has abstractyes

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