Beyond the Schools of Psychology 1: A Digital Analysis of <i>Psychological Review</i>, 1894–1903
Bibliographic record
Abstract
Traditionally, American psychology at the turn of the twentieth century has been framed as a competition among a number of "schools": structuralism, functionalism, behaviorism, etc. But this is only one way in which the "structure" of the discipline can be conceived. Most psychologists did not belong to a particular school, but they still worked within loose intellectual communities, and so their work was part of an implicit psychological "genre," if not a formalized "school." In this study, we began the process of discovering the underlying genres of American psychology at the turn of the twentieth century by taking the complete corpus of articles from the journal Psychological Review during the first decade of its publication and conducting a statistical analysis of the vocabularies they employed to see what clusters of articles naturally emerged. Although the traditional functionalist school was among the clusters we found, we also found distinct research traditions around the topics of color vision, spatial vision, philosophy/metatheory, and emotion. In addition, momentary clusters corresponding to important debates (e.g., the variability hypothesis) appeared during certain years, but not others.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".