Commentary for special issue of <i>Applied Cognitive Psychology</i> in honor of Alan Scoboria
Bibliographic record
Abstract
Summary The world would be a better place if there were more people like Alan. What an extraordinary convergence of kindness, reflectiveness, persistence, ethics, breadth of knowledge, and raw smarts! He positively and substantially contributed to cognitive, clinical, and social psychology, and to the lives of his students and collaborators. This collection carries that legacy forward. Alan would have relished reading every one of these articles. I am honored by the opportunity to comment. I hope to do so in a way that Alan would approve. I begin with a brief reflection on one of the “big picture” ideas in Alan's work. Then I discuss several issues that I know were near and dear to Alan and that have also occupied my own thinking for years. In addition to scientific accounts of true and false beliefs and memories, my comments are informed by the methodological reform movement. As an oldster, I also take this opportunity to mention a few earlier issues/findings that relate to current controversies.
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.
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 | 0.008 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.026 | 0.042 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".