Rehabilitation culture and its impact on technology: unpacking practical conditions for ultrabilitation
Bibliographic record
Abstract
Purpose: It has been proposed that rehabilitation practice expand its aims beyond recovery to “ultrabilitation,” but only if certain biological, technological, and psychosocial conditions are met. There is thus an opportunity to connect ultrabilitation, as a concept, to adjacent literature on assistive technology and sociotechnical systems.Method: We draw on insights from sociology of technology and responsible innovation, as well as concrete examples of neural devices and the culture of rehabilitation practice, to further refine our understanding of the conditions of possibility for ultrabilitation.Results: “Assistive” technologies can indeed be re-imagined as “ultrabilitative,” but this shift is both psychosocial and technological in nature, such that rehabilitation professionals will likely play a key role in this shift. There is not, however, sufficient evidence to suggest whether they will support or hinder ultrabilitative uses of technology.Conclusion: Advancing the idea and project of ultrabilitation must be grounded in a nuanced understanding of actual rehabilitation practice and the norms of broader society, which can be gained from engaging with adjacent literatures and by conducting further research on technology use in rehabilitation contexts.Implications for rehabilitation“Assistive” technologies can be conceptually re-imagined as “ultrabilitative” technologies, expanding their utility from recovery to enhancement and flourishing.Actual development and use of ultrabilitative technology is both a technical and psychosocial challenge, and its success depends on the cultural context in which technology is situated.Further empirical research is needed on the ways in which rehabilitation culture and the norms of broader society might impact or even inhibit the use of ultrabilitative technology.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.090 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".