Sliding into happiness: A new tool for measuring affective responses to words.
Bibliographic record
Abstract
Reliable measurement of affective responses is critical for research into human emotion. Affective evaluation of words is most commonly gauged on multiple dimensions-including valence (positivity) and arousal-using a rating scale. Despite its popularity, this scale is open to criticism: It generates ordinal data that is often misinterpreted as interval, it does not provide the fine resolution that is essential by recent theoretical accounts of emotion, and its extremes may not be properly calibrated. In 5 experiments, the authors introduce a new slider tool for affective evaluation of words on a continuous, well-calibrated and high-resolution scale. In Experiment 1, participants were shown a word and asked to move a manikin representing themselves closer to or farther away from the word. The manikin's distance from the word strongly correlated with the word's valence. In Experiment 2, individual differences in shyness and sociability elicited reliable differences in distance from the words. Experiment 3 validated the results of Experiments 1 and 2 using a demographically more diverse population of responders. Finally, Experiment 4 (along with Experiment 2) suggested that task demand is not a potential cause for scale recalibration. In Experiment 5, men and women placed a manikin closer or farther from words that showed sex differences in valence, highlighting the sensitivity of this measure to group differences. These findings shed a new light on interactions among affect, language, and individual differences, and demonstrate the utility of a new tool for measuring word affect. (PsycINFO Database Record
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".