Bibliographic record
Abstract
The purpose of this article is to present a critical analysis of current research practices in the study of behavior from the point of view of scientific realism. Although the so-called “replication crisis” observed in the psychological and health sciences has led to various proposals for improving research quality, most of those proposals take the standard linear input–output approach for granted, where behavioral variability is seen as the result of uncontrolled random variables hiding the true input–output relations. Aggregate data and the computation of sample statistics are used to estimate population parameters, the true reality behind appearances. In this paper, I offer a different interpretation: variability is a fact of behavior necessary for successful performance, not the result of some unknown variables randomly affecting individual outputs. Research models that take individual behavior with all of its complexity as the real thing, can help us overcome the limitations of the standard approach to research. As an illustration, I also describe two approaches to behavioral investigations that do not rely on standard statistical analysis for producing genuine knowledge: perceptual control theory and observation-oriented modeling.
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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 | 0.173 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.084 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".