Proliferation of measures contributes to advancing psychological science
Bibliographic record
Abstract
It is old news that psychology is going through a serious replication and credibility crisis. In searching for solutions, several phenomena have been pointed out as potential causes 1 : overemphasis on statistical significance, publication bias, inadequate statistical power, weak specification of theories and analysis plans, etc. A currently much-debated issue is the proliferation and variability of measures that are typically found in psychological assessment 2 . The scientific community is concerned that such proliferation may lead to questionable measurement practices 3 and has therefore recommended guidelines to counter the proliferation of trivial and redundant measures 4 . Such guidelines suggest that we should, for example, aspire to demonstrate non-redundancy, report and justify modifications in scales, and provide evidence on different sources of validity (including incremental validity) for any new or modified instrument. Following these guidelines may alleviate the phenomenon to some extent, but we expect and support the proliferation of psychological measures to continue because of its relevance for theory development and validation.
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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 | 0.428 | 0.691 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.020 | 0.049 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".