Categorising emotion words: the influence of response options
Bibliographic record
Abstract
Abstract Words used to describe emotion are influenced by experience, context and culture; nevertheless, research studies often constrain participant response options. We explored the influence of response options on how people conceptualise emotion words in two cross-sectional studies. In Study 1 participants rated the degree to which a large set of emotion words (n = 497) fit five basic emotion categories – Happy, Sad, Angry, Fearful, Neutral. Twenty-four words that fit well within these categories were included in Study 2. In Study 2 response options were expanded to include two additional basic emotions (Disgust, Joy), and six complex emotions (Amusement, Anxiety, Contentment, Irritated, Pride, Relief). Only half of the Study 1 words were categorised into the same emotion categories in Study 2. An increase in diversity of ratings for both positive and negative valenced words suggested overlaps in people’s conceptualisations of emotion words. Results suggest potential benefits of providing research participants complex emotion categories of varying intensity, which may better reflect people’s nuanced conceptualisations of emotion. Future research exploring varied response options may provide further insight into how people categorise and differentiate emotion words.
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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 | 0.029 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".