Constructing narratives to describe video events using aided communication
Bibliographic record
Abstract
Narratives are a pervasive form of discourse and a rich source for exploring a range of language and cognitive skills. The limited research base to date suggests that narratives generated using aided communication may be structurally simple, and that features of cohesion and reference may be lacking. This study reports on the analysis of narratives generated in interactions involving aided communication in response to short, silent, video vignettes depicting events with unintended or unexpected consequences. Two measures were applied to the data: the Narrative Scoring Scheme and the Narrative Analysis Profile. A total of 15 participants who used aided communication interacted with three different communication partners (peers, parents, professionals) relaying narratives about three video events. Their narratives were evaluated with reference to narratives of 15 peers with typical development in response to the same short videos and to the narratives that were interpreted by their communication partners. Overall, the narratives generated using aided communication were shorter and less complete than those of the speaking peers, but they incorporated many similar elements. Topic maintenance and inclusion of scene-setting elements were consistent strengths. Communication partners offered rich interpretations of aided narratives. Relative to the aided narratives, these interpreted narratives were typically structurally more complete and cohesive and many incorporated more elaborated semantic content. The data reinforce the robust value of narratives in interaction and their potential for showcasing language and communication achievements in aided communication.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".