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
Many psychological dimensions seem bipolar (e.g., happy-sad, optimism-pessimism, and introversion-extraversion). However, seeming opposites frequently do not act the way researchers predict real opposites would: having correlations near -1, loading on the same factor, and having relations with external variables that are equal in magnitude and opposite in sign. We argue these predictions are often incorrect because the bipolar model has been misspecified or specified too narrowly. We therefore explicitly define a general bipolar model for ideal error-free data and then extend this model to empirical data influenced by random and systematic measurement error. Our model shows the predictions above are correct only under restrictive circumstances that are unlikely to apply in practice. Moreover, if a bipolar dimension is divided into two so that researchers can test bipolarity, our model shows that the correlation between the two can be far from -1; thus, strategies based upon Pearson product-moment correlations and their factor analyses do not test if variables are opposites. Moreover, the two parts need not be mutually exclusive; thus, measures of co-occurrence do not test if variables are opposites. We offer alternative strategies for testing if variables are opposites, strategies based upon censored data analysis. Our model and findings have implications not just for testing bipolarity, but also for associated theory and measurement, and they expose potential artifacts in correlational and dimensional analyses involving any type of negative relations. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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