A call for renewed attention to construct validity and measurement in psychopathology research
Bibliographic record
Abstract
Psychopathologists have failed to make significant progress toward understanding the causes of psychopathology. Despite the foundational importance of construct validity and measurement to our field, insufficient attention is paid to these concerns in the assessment of psychopathology vulnerabilities prior to their implementation in causal models. I review the current state of construct validity and measurement in psychopathology research, highlighting the lack of consensus regarding how we should define and measure vulnerability constructs. The limited capacity of open science practices to address these definitional and measurement challenges is discussed. Recommendations for progress are made, including the need for consensus agreement on (1) working definitions and (2) measures of vulnerability constructs. Other recommendations include (3) the need to incentivize 'pre-clinical' descriptive work focused on measurement development, (4) the formation of open-access databases designed to facilitate measurement evaluation and development, and (5) increased exploration of the use of novel technologies to facilitate the collection of high-quality measures of vulnerability.
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 | 0.165 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.029 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".