The potential of studying specific language impairment in bilinguals for linguistic research on specific language impairment in monolinguals
Bibliographic record
Abstract
In her Keynote Article, Paradis discusses the role of the interface between bilingual development and specific language impairment (SLI) on two different levels. On the level of theoretical explanations of SLI, Paradis asks how domain general versus domain-specific perspectives on SLI can account for bilingual SLI, as well as what bilingual SLI may contribute to the discussion of these theories. Paradis argues in favor of domain-specific deficits (in addition to well-documented processing deficits in SLI), and especially for the maturational model (Rice, 2004). She argues against a mere processing deficit of input information and against deficits in working memory and processing speed as sole sources of SLI. On the practical level, Paradis focuses on the question of whether and how language tests that have been standardized for monolingual children are valid for the assessment of SLI in bilingual children. Both discussions, on theory and on practice, are based on empirical data from Canadian studies on bilingual children with and without SLI carried out by Johanne Paradis, Martha Crago, and Fred Genesee (e.g., Crago & Paradis, 2003; Paradis, 2007; Paradis, Crago, & Genesee, & Rice, 2003).
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".