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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".