Rethinking posthumanism in rehabilitation science: Lessons from Indigenous, Black, and decolonial thought
Bibliographic record
Abstract
Posthumanism is a theoretical paradigm in Western continental philosophy with emerging significance and popularity in the health disciplines. Rehabilitation science scholars in fields like occupational therapy and physical therapy have taken up posthumanism, valuing its interventions into the harms of European humanist conceptualizations of the “(hu)man” which perpetuate individualism, ableism, and anthropocentrism. This paper responds to the pervasive use of posthumanism in the rehabilitation science literature—particularly among white scholars in the “Global North”—and its omission of sustained engagements with forms of de humanization (specifically racism, colonialism, and anti-Blackness). For posthuman healthcare and rehabilitation scholarship to have utility beyond white, globally elite populations, we invite fellow rehabilitation science scholars to engage with the important critiques of posthumanism made by Black, Indigenous, and Latin American decolonial scholars. We synthesize these critiques and warnings about the forms of epistemic colonial violence embedded within popular approaches to posthumanism, and query rehabilitation scholars’ responsibilities to pause and center theories of the human and posthuman that have long been developed and lived by racialized and Indigenous scholars, activists, and knowledge holders.
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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 | 0.019 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.108 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| 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".