Identifying First Nations students with invisible disabilities
Bibliographic record
Abstract
Countless studies have employed the term disabilities as an all-encompassing umbrella for both visible and invisible disabilities. Scholars and practitioners have relied on medical criteria and corresponding diagnostic tools as the standard for verification of all disabilities. However, invisible disabilities are difficult to identify, not only because they are not obvious, but also because they represent a wide range of characteristics, fall along continuums of severity for certain disabilities, and lack the verification criteria and diagnostic tools available for visible disabilities. In addition, difficulties and obstacles for students with invisible disabilities are experienced daily in the educational environments of First Nations schools, and little is known about these students and their experiences. Are teachers working in First Nations schools able to identify students with invisible disabilities? Are the teachers cognizant of invisible disabilities? Do the teachers understand the impacts of invisible disabilities on students’ identity, learning and socialization? This study focused on establishing a baseline of teachers’ knowledge of invisible disabilities. One goal was to develop a protocol for identifying First Nations students with invisible disabilities. Teacher questionnaires and resource teacher interviews were employed to gather data. As this study explored a new area, great care was taken to record all aspects of the research with the hope that other researchers will further explore the connection between identification of First Nations students with invisible disabilities and appropriate educational programming for them. This study’s findings exposed the presence of a vast gap in teachers’ understandings of exactly what invisible disabilities encompass. Something as seemingly simple as consensus on the categorization of disabilities as visible or invisible, by the teachers in this study, was absent. Other areas connected to identifying invisible disabilities were equally blurred. Many avenues for future research were suggested by the data collected, but the recommendations based on this study’s findings focus on: (a) what actions are needed to develop and implement an early identification protocol for students with invisible disabilities, (b) how to create better teacher awareness of all aspects of invisible disabilities, from definition to barriers to presenting characteristics, (c) how to organize professional development to improve FN teachers’ awareness of invisible disabilities as well as knowledge of effective adaptations and accommodations, and (d) how to implement regular basic screenings that can assist in identifying FN students with invisible disabilities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".