The Ouroboros of Psychological Methodology: The Case of Effect Sizes (Mechanical Objectivity vs. Expertise)
Bibliographic record
Abstract
The reporting and interpretation of effect sizes is often promoted as a panacea for the ramifications of institutionalized statistical rituals associated with the null-hypothesis significance test. Mechanical objectivity—conflating the use of a method with the obtainment of truth—is a useful theoretical tool for understanding the possible failure of effect size reporting ( Porter, 1995 ). This article helps elucidate the ouroboros of psychological methodology. This is the cycle of improved tools to produce trustworthy knowledge, leading to their institutionalization and adoption as forms of thinking, leading to methodologists eventually admonishing researchers for relying too heavily on rituals, finally leading to the production of more new improved quantitative tools that may follow along this circular path. Despite many critiques and warnings, research psychologists’ superficial adoption of effect sizes might preclude expert interpretation much like in the null-hypothesis significance test as widely received. One solution to this situation is bottom-up: promoting a balance of mechanical objectivity and expertise in the teaching of methods and research. This would require the acceptance and encouragement of expert interpretation within psychological science.
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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 | 0.607 | 0.716 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.008 | 0.182 |
| Scholarly communication | 0.022 | 0.045 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.022 | 0.032 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".