Who Needs to Fit in? Who Gets to Stand out? Communication Technologies Including Brain-Machine Interfaces Revealed from the Perspectives of Special Education School Teachers Through an Ableism Lens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Some new and envisioned technologies such as brain machine interfaces (BMI) that are being developed initially for people with disabilities, but whose use can also be expanded to the general public have the potential to change body ability expectations of disabled and non-disabled people beyond the species-typical. The ways in which this dynamic will impact students with disabilities in the domain of special education is explored. Data was drawn from six special education school teachers from one school in Calgary, Alberta. Five sub-themes (social acceptance, not adding to the impairment, fear of judgement by society, pursuing “normality” and meeting the demands of society) were identified that fit under the main identified theme of “fitting in by not standing out”. Findings demonstrate a dichotomy in participant views of non- or socially acceptable communication devices. The perception of BMI technology was also explored among special education school teachers, revealing benefits and challenges with the uptake of this technology for students with disabilities. Perceptions of people with disabilities and ableism are presented as conceptual frameworks to interpret the influence and impact of the findings.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it