A Comparison of Two Lists Providing Emotional Norms for English Words (ANEW and the DAL)
Why this work is in the frame
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Bibliographic record
Abstract
Although different in terms of purpose, word-selection procedures, and rating scales, both the ANEW (n = 1034) and DAL (n = 8742) lists, which have 633 words in common, provide normative emotional ratings for English words. This research compared the lists and cross-validated the two main lexical dimensions of affect. Parallel representatives of the two dimensions (Valence and Pleasantness, Arousal and Activation) were correlated across lists (rs = .86, .63). In tune with their separate purposes, the ANEW list, which was designed to describe emotional words, included more rare words, while the DAL, which was designed for natural language applications, included more common ones. The Valence-Activation scatterplot for ANEW was C-shaped and included fewer Arousing words of medium Valence, such as "awake," "debate," and "proves," while the DAL included fewer less common words descriptive of emotion such as "maniac," "corrupt," and "lavish." In view of these differences, list similarities strongly support the generalizability of the two main lexical dimensions of affect.
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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 | 0.001 |
| 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 | 0.000 |
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