A critical re-analysis of six implicit learning papers
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
We present a critical re-analysis of six implicit learning papers published by the same authors between 2010 and 2021. We calculated effect sizes for each pairwise comparison reported in the papers using the data published in each article. We further identified mathematically impossible data reported in multiple papers, either with deductive logic or by conducting a GRIMMER analysis of reported means and standard deviations. We found the pairwise effect sizes were implausible in all six articles in question, with Cohen’s d values often exceeding 100 and sometimes exceeding 1000. In contrast, the largest effect size observed in a million simulated experiments with a true effect of d = 3 was d = 6.6. Impossible statistics were reported in four out of the six articles. Reported test statistics and eta2 values were also implausible, with several eta2 = .99 and even eta2 = 1.0 for between-subjects main effects. The results reported in the six articles in question are unreliable. Many of the problems we identified could be spotted without further analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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 it