Linguistic risk-taking in inclusive contexts: The case of Developmental Language Disorder (DLD)
Bibliographic record
Abstract
A Linguistic Risk-Taking Passport is an excellent means to orchestrate Task-Based Language Teaching (TBLT) and different learner needs in inclusive settings. We identified core areas in which learners with Developmental Language Disorder (DLD) face linguistic risk-taking. Two semi-structured expert interviews were conducted and qualitative content analysis was carried out to identify specific linguistic risks of learners with DLD in and outside of school. Our results suggest that learners with DLD need systematic support in choosing and facing the next appropriate linguistic risks that lead to healthy risk-taking. We argue that this can contribute to an exploitation of their learning potential and to social-emotional well-being. Numerous linguistic risks are related to an increased sensitivity in the affective domain. The data analysis led to tentative hypotheses for a Linguistic Risk-Taking Passport for learners with DLD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".