Sense and nonsense in psychological measurement: A case of problem and method passing one another by
Bibliographic record
Abstract
There exists conceptual confusion in the psychological measurement literature. One such example is the nonsense of the question, “are psychological attributes quantitative?” when referring to nontechnical psychological phenomena. Rather than debating whether this is an empirical or conceptual question, the issue of concern should be whether it makes sense to ask it. As illuminated by Wittgenstein almost a century ago, the concepts of psychology are everyday concepts rather than technical ones. However, the enduring comparison of psychology with the physical sciences leads to the adoption of technical language that loses its meaning when used with the aim of explaining everyday concepts. For this reason, asking whether psychological attributes are quantitative is, in many cases, a nonsensical question to ask.
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 | 0.246 | 0.322 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.013 | 0.176 |
| Scholarly communication | 0.024 | 0.062 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.020 | 0.035 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".