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
Abstract The issue of content overlap has the potential to influence the results of any psychological researcher examining the relationship between two related variables. Instances of content overlap can be split into two groups; the first involves construct overlap , where an unclear boundary exists between two constructs, and the second involves measurement overlap , where a measure is potentially contaminated with items that better assess a different construct. There are several methodological and statistical ways to further assess the nature of content overlap. These include thoughtful content analysis and an examination of inter‐item correlations. If content overlap exists, there are several methods available for researchers to employ. One set of methods involves the deletion of conceptually or empirically redundant items. These techniques should be used with caution, as they are based on subjective interpretations and item deletion comes with many methodological and theoretical pitfalls. The gold standard method involves the use of hierarchical factor models. In this method, the common and specific variance between the general construct and the potentially overlapping content is separated and then relationships can be examined.
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 | 0.028 | 0.262 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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