The multimodal marking of aspect: The case of five periphrastic auxiliary constructions in North American English
Bibliographic record
Abstract
Abstract Cognitive linguistics (CL) has, in recent years, seen an increase in appeals to include multiple modalities in language analyses. While individual studies have incorporated gesture, gaze, facial expression, and prosody, among other modalities, CL has yet to completely embrace the systematic analysis of face-to-face interaction. Here, I present an investigation of five aspect-marking periphrastic constructions in North American English. Using naturalistic interactional data (n=250) from the Red Hen archive, this study establishes a multimodal profile for auxiliary constructions headed by one of five highly aspectualized verbs: continue, keep, start, stop, and quit, as in The jackpot continued to grow and He quit smoking . Results show that gesture timing, the structure of the gesture stroke, and gesture movement type, are variables that iconically and differentially represent distinctive aspectual conceptualizations. This study enhances our understanding of aspectual representation in co-speech gesture and informs the ongoing debate within CL and construction grammar circles of what constitutes conventionalization, or what constitutes a construction (mono- or multimodal).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".