Deconstructing language practices: discursive constructions of children in Individual Education Plan resource documents
Bibliographic record
Abstract
Although Individual Education Plan (IEP) resource documents in Ontario, Canada aim to assist children in achieving their special educational goals, a point of disjuncture exists between the documents’ intentions and children’s actual experiences. Addressing this issue is crucial in preventing inequity and fostering educational development and social well-being for children. We employ critical discourse analysis informed by disability theory to deconstruct the language practices used to conceptualize children in IEP resource documents. Our purpose is to question the underlying assumptions regarding representations of children and illuminate the potentially harmful consequences of such conceptions. We expose the presence of both neutral and harmful language practices and consider how such language may shape the way the documents translate from policy to practice. This study offers a model through which the language of other special education documents can be critically evaluated and proposes potential avenues for creating documents that avoid disabling children further.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.023 | 0.091 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| 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".