Aspect and Narrative Event Segmentation
Bibliographic record
Abstract
Time is central to human cognition, both in terms of how we understand the world and the events that unfold around us as well as how we communicate about those events. As such, language has morphological systems, such as temporal adverbs, tense, and aspect to convey the passage of time. The current study explored the role of one such temporal marker, grammatical aspect, and its impact on how we understand the temporal boundaries between events conveyed in narratives. In Experiments 1 and 2, participants read stories that contained a target event that was either conveyed with a perfective (e.g., watched a movie) or imperfective aspect (e.g., was watching a movie) and engaged in an event segmentation task. Events described in the perfective aspect were more often perceived as event boundaries than events in the imperfective aspect, however, event duration (long vs. short) did not impact this relationship in Experiment 2. Experiment 3 demonstrated that readers were sensitive to grammatical aspect and event duration in the context of a story continuation task. Overall this study demonstrates that grammatical aspect interacts with world knowledge to convey event structure information that influences how people interpret the end and beginning of events.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".