The impact of pragmatic markers and hedging on sentence comprehension: a case study of<i>comme</i>and<i>genre</i>
Bibliographic record
Abstract
ABSTRACT Current research on conceptual and semantic representations is mainly based on prototypical word classes, such as nouns and verbs. Hence, most models of language processing and language representation rely on experimental investigations on these word classes. Until today, only a few psycholinguistic studies centre on the processing of pragmatic markers and hedges and their effect on speech comprehension. The present article aims to give experimental evidence for the processing of semantic meaning patterns and pragmatic functions of pragmatic markers. The focus will be on the question, if pragmatic markers and hedges play a role in sentence processing. This main problem will be illustrated and discussed by means of experimental data. In a monolingual sentence verification task with lexical decision, the meaning patterns and functions of the partially equivalent pragmatic French markers comme and genre are investigated in Canadian and European French. The results of the sentence word verification task provide evidence for an impact of pragmatic functions and semantic meaning patterns of pragmatic markers on sentence processing.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".