The long past and short history of the vocabulary of anglophone psychology.
Bibliographic record
Abstract
How do particular words come to be part of the vocabulary of Anglophone psychology? The present study sampled 600 words with psychological senses from the Oxford English Dictionary, which not only gives the number of senses for each word but also the date and author for the earliest known occurrence of each sense. Analogous information for the same words was taken from PsycINFO. One can distinguish between words for which their psychological sense is the first to occur in the history of the written language (primary psychological words) and words for which their psychological sense only emerges after one or more other senses have become established in the written language (secondary psychological words). To use a distinction made famous by Ebbinghaus, secondary psychological words have both a past and a history in psychology, while primary psychological words only have a history. Secondary psychological words have more connections to other words and occur more frequently in PsycINFO than do primary psychological words. For secondary psychological words, it is possible to trace a process of metaphoric polysemy that provides a basis for the eventual occurrence of the psychological sense of a word. Some primary psychological words are now developing secondary, nonpsychological senses, showing that they are subject to the same metaphoric process as are any other words.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".