Linguistically diverse assessment : a Canadian context
Bibliographic record
Abstract
In Canada, there are currently no standardized national guidelines for the psychoeducational assessment of linguistically diverse individuals. It is the role of Canadian psychologists and psychological associates to adopt psychoeducational assessment practices that mitigate the historic over-referral to special education programs of students who are linguistically diverse, the misdiagnosis of these students, and the mismatch of educational services and supports to these students. The potential over-referral of these students for special education programming may be derived from a lack of understanding of language acquisition factors, an over-reliance on standardized tests with norms that may not reflect the diverse populations that psychologists are working with as well as a lack of standardized national assessment guidelines for practitioners to follow. This over-referral is a human rights issue and is potentially made worse by the lack of standardized guidelines, as these students may benefit more from the support of their language acquisition rather than special education services. In this study, an online survey of 73 practitioners conducting psychoeducational assessments for the purpose of supporting Canadian students was conducted to better understand what practices practitioners are employing when working with linguistically diverse clients. Respondents to the survey reported using a variety of assessment methods. Of the various assessment methods reported by surveyed practitioners, some align with the literature on ensuring appropriate assessment of linguistically diverse clients, while other do not.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".