Reductionism in the Comments and Autobiographical Accounts of Prominent Psychologists
Bibliographic record
Abstract
Many of the researchers in the field of psychological science use strategies and methods in which human actions and experiences are reduced to behavioral contingencies, statistical regularities, neurophysiological states and processes, and computational functions and models. However, many psychologists talk readily and easily about how their research might assist human agents to solve problems, cope, make decisions, self-regulate, and more generally "make a difference" and "take control." The authors considered informally selected comments by several eminent psychologists, and more formally, 73 autobiographical accounts of prominent psychologists to see what could be learned about the attitudes of these psychologists toward reductionism in their own work and in the field of psychology in general. In interpreting these comments and accounts, the authors posit a gap between many psychologists' contemplation of their work and their actual research practices. The authors also suggest that such a gap may be related to psychologists' educational experiences and their scholarly and professional socialization, as well as to their subdisciplinary attachments and contexts.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.018 |
| 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".