Bibliographic record
Abstract
Abstract S. S. Stevens was one of a number of prominent psychologists who published seminal articles in The American Journal of Psychology (AJP). Indeed, the first or, arguably, most important articles in several of his research strands were published there. In this brief treatment of his monumental work, I review these articles and some of their sequelae, both in Stevens’s own work and in that of others, in an attempt to sketch out how Stevens’s contributions in AJP helped form the development of experimental sensory and perceptual psychology throughout the 20th century. I focus on his work in psychophysical scaling, because in my opinion that has been his most important legacy. Indeed, the article that probably generated the flurry of work in psychophysical scaling that persisted into the 1990s was a brilliant work published in 1956 in AJP. In that article Stevens not only demonstrated the validity and reliability of direct scaling (in this case magnitude estimation and production) but also investigated a range of factors that could affect its results, anchoring the later work that led to its adoption as the fundamental and most popular approach to psychophysical scaling still in use today. In this section I also expand on a few of the modern directions in which this work has gone. Stevens also published in AJP classic articles on the localization of sound, the dimensions of sound, the relation of volume to intensity, and the neural quantum in pitch and loudness discrimination. He even contributed an article on scaling coffee odor. His work is a stellar example of how AJP has influenced psychological science then and now.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".