Amplifying issues related to psychological testing and assessment.
Bibliographic record
Abstract
February 2002 • American Psychologist able to improve on the validity of their assessment conclusions (Garb, 1998; Grove, Zald, Lebow, Snitz, & Nelson, 2000). Because Meyer et al. (2001) provided an overly optimistic evaluation of current psychological assessment practices, many readers of their article are likely to conclude that the scientific status of psychological assessment is firmly established. Unfortunately, nothing could be further from the truth. A more accurate conclusion is that very little is known about the validity or utility of psychological assessment. This does not mean that psychological assessment is without merit; rather, it indicates that, as with so many aspects of psychological practice, psychologists lack scientific evidence that bears on assessment’s value. Psychologists must build a science of assessment, not just a body of research on tests and test subscales. If psychological assessment is to be promoted on the basis of science, it must be on the basis of relevant studies of assessment, not on unwarranted extrapolations from the literature on test validity.
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 | 0.129 | 0.367 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.012 | 0.028 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".