Unraveling time in communicative interactions involving children who use aided communication
Bibliographic record
Abstract
Time use and timing are of particular relevance for people who use communication aids because of the role time plays in communication. However, the use of time in real-life communicative interactions of aided communicators has not been much researched. The present study explores time use in goal-oriented and activity-based communicative interactions involving 72 children who used aided communication and 56 children who used natural speech, aged 5-15 years, and their communication partners. The children using aided communication took significantly longer time than their naturally speaking peers to complete the tasks using language. Access method, whether direct or scanning, did impact aided communicators' time use, with children using direct access being faster than children using scanning. Time use was not statistically related to age or verbal comprehension but was related to non-verbal reasoning: to communicate with their partners, children with higher non-verbal reasoning scores used less time than children with lower reasoning scores. Regardless of access method, aided communicators who used less time to communicate had more success in solving the tasks. The results suggest that to tackle the issue of time, aided language interventions with children could focus on communicative problem-solving with partners in real-life situations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".