What Does It Mean to Be “Utterly Content”? Semantic Prosody Impacts Nuanced Inferences Beyond Just Valence
Bibliographic record
Abstract
Words have semantic prosody when they collocate with positive/negative concepts in natural language. Semantic prosody encourages positive/negative evaluations. However, it is unknown whether semantic prosody affects inferences of other attributes aside from positivity/negativity. Semantic prosody likely causes people to expect the valence of what comes next, and expectation violations occur when authors have ironic intent and when authors lack fluency with a language. Four studies investigated whether semantically prosodic expectations impact specific inferences about authors. Participants perceived a writer as having greater ironic intent when the writer used a sentence with a semantically prosodic word that mismatched with the valence of adjacent words (Studies 1, 3, and 4). Additionally, in line with English as foreign language pedagogy, the same manipulation caused participants to perceive a writer as being less fluent in English (Studies 2, 3, and 4). Thus, semantic prosody generates expectations that affect nuanced inferences.
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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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".