Publish and Perish: Psychology’s Most Prolific Authors Are Not Always the Ones We Remember
Bibliographic record
Abstract
What is the relationship between being highly prolific in the realm of publication and being remembered as a great psychologist of the past? In this study, the PsycINFO database was used to identify the historical figures who wrote the most journal articles during the half-century from 1890 to 1939. Although a number of the 10 most prolific authors are widely remembered for their influence on the discipline today-E. L. Thorndike, Karl Pearson, E. B. Titchener, Henri Pi6ron-the majority are mostly forgotten. The data were also separated into the 5 distinct decades. Once again, a mixture of eminent and obscure individuals made appearances. Most striking, perhaps, was the great increase in articles published over the course of the half-century-approximately doubling each decade-and the enormous turnover in who was most prolific, decade over decade. In total, 100 distinct individuals appeared across just 5 lists of about 25 names each.
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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 | 0.005 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".