Self- and Other-Repairs in Child-Adult Interaction: A Case Study of a Pair of Persian-Speaking Twins
Bibliographic record
Abstract
Twenty-five years ago, Schegloff (1989) proposed that repair is the most crucial factor in understanding the nature of language development. By observing and examining the repairs children make, not only can we understand repair organization, but also children language development and cognitive stage. Research in syntactic structure of repair, self-initiated self-repair (SISR) or other-repair have gained enough attention in recent years through the works of Forrester (2008), Radford (2008), and Morgenstern, Leroy, & Caef (2013). Some studies analyzed both self-repair and other-repair (Morgenstern et al., 2013; Salonen & Laakso, 2009; Forrester, 2008), whereas a few other studies analyzed only other-repairs from the perspective of parents (Huang, 2011). There are many studies done regarding the incidence of self-repair over other-repair (Schegloff et al., 1977); the relationship between repair and turn (Schegloff, 1988); corrective feedback (Laakso & Soininen, 2010); other-repetition (Huang, 2011); and adult’s self-repair (Laakso & Sorjonen, 2010). However, there is some inconsistency in their findings. The data for this study comprised four video-recorded adult-child interactions at a children’s home in various interactional activities (role-play, short story, or watching cartoons. The purpose of this study is to examine the incidence of self- and other-repairs in the language acquisition process of Persian children and to investigate if there is a relationship between child’s self-repair and adult’s other-repair.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".