Affective biases in English are bi-dimensional
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
A long-standing observation about the interface between emotion and language is that positive words are used more frequently than negative ones, leading to the Pollyanna hypothesis which alleges a predominantly optimistic outlook in humans. This paper uses the largest available collection of affective ratings as well as insights from linguistics to revisit the Pollyanna hypothesis as it relates to two dimensions of emotion: valence (pleasantness) and arousal (intensity). We identified systematic patterns in the distribution of words over a bi-dimensional affective space, which (1) run counter to and supersede most prior accounts, and (2) differ drastically between word types (unique, distinct words in the lexicon) and word tokens (number of occurrences of available words in the lexicon). We argue for two factors that shape affect in language and society: a pro-social benevolent communication strategy with its emphasis on useful and dangerous phenomena, and the structure of human subjective perception of affect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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