Two-dimensional affective space: A new approach to orienting the axes.
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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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".