Science or not, conceptual problems remain: Seeking conceptual clarity around “psychology as a science” debates
Bibliographic record
Abstract
We argue that the question, “Should psychology follow the methods and principles of the natural sciences?” is not one that should be answered by theoretical psychologists or metascientists; rather, we implore psychological researchers themselves to heed Wittgenstein’s observation that a preoccupation with method and principles risks overlooking important conceptual issues, the clarification of which are necessarily antecedent to consideration of empirical activities. We examine potential conceptual problems that arise when psychological researchers attempt to follow natural science methods and principles without first considering the concepts that are relevant to their scientific pursuits. Drawing primarily from the works of Hacker, Lamiell, and Maraun, we argue against the dogmatic following of any methods or sets of principles. Instead, we posit that natural science methods and principles should be considered on a case-by-case basis after relevant psychological concepts have been carefully considered and empirical investigation has been deemed an appropriate path forward.
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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 | 0.070 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.013 | 0.127 |
| Scholarly communication | 0.022 | 0.051 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.014 | 0.025 |
| 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".