The vocabulary of anglophone psychology in the context of other subjects.
Bibliographic record
Abstract
Anglophone psychology shares its vocabulary with several other subjects. Some of the more obvious subjects that have parts of their vocabulary in common with Anglophone psychology include biology (e.g., dominance), chemistry (e.g., isomorphism), philosophy (e.g., phenomenology), and theology (e.g., mediator). Using data from the Oxford English Dictionary as well as other sources, the present study explored the history of these common vocabularies, with a view to broadening our understanding of the relation between the history of psychology and the histories of other subjects. It turns out that there are at least 156 different subjects that share words with psychology. Those that have the most words in common with psychology are mathematics, biology, physics, medicine, chemistry, philosophy, law, music, linguistics, electricity, pathology, and computing. Words that have senses in other subjects and have their origins in ordinary language are used more frequently as PsycINFO keywords than words that were invented specifically for use in psychology. These and other results are interpreted in terms of the ordinary language roots of the vocabulary of Anglophone psychology and other subjects, the degree to which operational definitions have determined the meaning of the psychological senses of words, the role of the psychologist in interdisciplinary research, and the validity of psychological essentialism.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".