Moment-By-Moment Reading of Proverbs in Literal and Nonliteral Contexts
Bibliographic record
Abstract
To date there has been very little research that has examined on-line reading of proverbs. This is surprising given that proverbs offer a unique opportunity to examine how different sources of information combine to constrain the resolution of statements that are ambiguous between a literal and nonliteral interpretation. The purpose of this research was to examine whether context plays an immediate role in constraining the meaning of a proverbial statement, or whether contextual effects come into play at a later stage of processing. Two self-paced moving window studies demonstrated that (a) context influenced resolution of the ambiguous meanings during the act of reading the proverb for both familiar and unfamiliar proverbs; (b) familiar proverbs are read more rapidly than unfamiliar proverbs, an effect that begins to emerge as early as the second word of the trope; and (c) whereas the reading times indicate that ambiguity in comprehension is resolved by the end of the sentence for familiar proverbs, for unfamiliar proverbs effects are still observed into the reading of the next sentence. The results are discussed in relation to existent models of nonliteral language processing, with constraint-based approaches to language processing suggested as a positive alternative.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".