Proliferation of measures contributes to advancing psychological science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it