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Record W7056460114

Evaluating intersections of vision impairment with stem research in Canada

2022· other· en· W7056460114 on OpenAlexaboutno aff

Bibliographic record

VenueOCAD University Open Research Repository (OCAD University) · 2022
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsAccommodationContext (archaeology)Visual impairmentQuarter (Canadian coin)Photo elicitationFocus groupLow vision
DOInot available

Abstract

fetched live from OpenAlex

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.
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\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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.637
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0030.003
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0090.000

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.

Opus teacher head0.067
GPT teacher head0.341
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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