Rebooting Inclusive Education? New Technologies and Disabled People
Bibliographic record
Abstract
This paper provides a speculative, conceptual and literature-based review of the relationship between disability and new technologies with a specific focus on inclusive education for disabled people. The first section critically explores disability and new technologies in a time of Industry 4.0. We lay out some concerns that we have, especially in relation to disabled people’s peripheral positionality, when it comes to these new developments. The second section focuses on the area of inclusive education. Inclusion and education are oftentimes in conflict with one another. We tease out these conflicts and argue that we cannot decouple the promise of new technologies from the challenges of inclusive education, because, in spite of the potential for technological mediation to broaden access to education, there remains deep-rooted problems with exclusion. The third section of our paper explores affirmative possibilities in relation to the interactions between disability and new technologies. We draw on the theoretical fields of Science and Technology Studies; Critical Disability Studies; Assistive and Inclusive Technologies; Collaborative Robotics, Maker and DIY Cultures and identify a number of key considerations that relate directly to the revaluing of inclusive education. We conclude our paper by identifying what we view as pressing and immediate concerns for inclusive educators when considering the merging of disability and technology, accessibility and learning design.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".