Bibliographic record
Abstract
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 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".