Evaluating intersections of vision impairment with stem research in Canada
Bibliographic record
Abstract
A quarter of a million Canadians have some sort of vision loss. To inform programs and policy about persons with visual impairments, I focused on research questions in the current Canadian literature: Looking at accommodations and barriers that persons with visual impairments confront when pursuing STEM learning; to identify deeper themes and gaps that present themselves; and use the gathered data as information that will lay the foundation for further research inquiries, improvements, and pedagogical change in educators for persons with visual impairment (PVI). A comprehensive search of databases (PubMed, Google Scholar, ERIC) was performed. Due to the minor criteria, the focus was on Canadian literature or the best available research on the keywords used. The findings highlighted themes that emerged in three different areas of accessibility, behaviour, and social settings for PVI. \n \nFurthermore, relationships were highlighted between the main themes and broken down further. This MRP discusses accommodation and the social and behavioural context in STEM education and identifies barriers faced by PVI and educators. While there is ample research on accessibility for PVI, more research needs to take place on lived experiences of PVI and to understand the behaviour and social aspects of engagement to ensure services of accommodations are met.
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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 | 0.018 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.034 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".