Two-dimensional affective space: A new approach to orienting the axes.
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
What are the constructs that underlie affective experiences? Some authors have suggested Valence and Activation, whereas others have suggested Positive Activation and Negative Activation-both approaches are represented by different axis orientations in traditional two-mode (People x Adjectives) factor analysis. The authors provide new evidence for this debate by using three-mode (People x Adjectives x Occasions) parallel factor (PARAFAC) analysis to determine the appropriate axes (and hence constructs) for representing affective experiences. Unlike traditional factor analysis, with PARAFAC different orientations of the axes fit the data differently so it is possible to determine the best fitting axes. In Study 1, the authors assessed the extent to which the PARAFAC procedure was able recover the axes defining a two-dimensional factor space under different conditions. In both Study 2 (N = 112) and Study 3 (N = 349), undergraduate students rated their emotional states on a variety of occasions. The best fitting axes for two-dimensional affective space were Valence and Activation in both studies. Exploration of higher dimensional solutions in Study 3 revealed a three-factor solution that, in addition to an activation factor, supported the separation of positive and negative emotions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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