Conceptual, methodological, and statistical issues in developmental psychopathology: A Special Issue in honor of Paul E. Meehl
Bibliographic record
Abstract
With the passing of Paul E. Meehl, Regents Professor of Psychology at the University of Minnesota, on February 14, 2003, the world lost one of the most influential clinical psychologists of the 20th century. The breadth of his interests, the preciseness and clarity of his thinking, the elegance of his writing, and his ability to integrate scientific and clinical matters of import were hallmarks of his illustrious career (see, e.g., Meehl, 1954, 1973, 1991). Yet, it is the very magnitude of his professional pursuits that defy categorization or even placement within a single field of inquiry. Whether they pertain to philosophical matters, measurement and psychodiagnostic issues, or elucidating psychopathological processes, Paul Meehl's contributions were seminal and established a base on which scholars could build their own theoretical and research perspectives. Although Paul certainly did not consider himself to be a developmental psychopathologist, his influence can be seen in the theoretical and methodological streams that have nurtured the emergence and growth of the field. Thus, it seems a fitting tribute to Paul that this Special Issue, “Conceptual, Methodological, and Statistical Issues in Developmental Psychopathology,” be dedicated in his honor.
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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 | 0.020 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.012 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".