The rise of quantitative methods in psychology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Quantitative methods have a long history in some scientific fields. Indeed, no one today would consider a qualitative data set in physics or a qualitative theory in chemistry. Quantitative methods are so central in these fields that they are often labelled “hard sciences”. Here, we examine the question whether psychology is ready to enter the “hard science club ” like biology did in the forties. The facts that a) over half of the statistical techniques used in psychology are less than 40 years old and that b) the number of simulations in empirical papers has followed an exponential growth since the eighties, both suggests that the answer is yes. The purpose of Tutorials in Quantitative Methods for Psychology is to provide a concise and easy access to the currents methods. The use of agreed-upon quantitative methods is maybe the most reliable defining feature of the so-called "hard sciences". This trend was initiated by Descartes in the study of optics and, with a greater impact, by Galileo in the study of motion over four centuries ago. By 1905, the mutation they initiated fully matured, yielding among other,
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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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it