Staying With the Trouble, a Rhizomatic Approach to Posthuman Methods: Assemblages and Becoming in the Posthuman Walking Project
Bibliographic record
Abstract
Persistent pain is the leading cause of years lived with disability worldwide. Research into pain experiences often adopts a humanistic perspective, predominantly relying on interview data and rarely engaging with real-world contexts. The Posthuman Walking Project brought together a transdisciplinary network of individuals with lived experiences of pain alongside academics and clinicians from five countries to collectively explore how posthuman philosophies might challenge human-centered paradigms. Specifically, we used mobile phone video footage to investigate the more-than-human entanglements of walking in the landscape when experiencing pain. This paper reflects on our engagement with the uncertainty and multifaceted nature of exploratory methods and how the process of “becoming posthuman” did not follow a pre-determined path. We outline our rhizomatic methodological approach, emphasizing the contributions of walker-partners, project development meetings, and the value of allowing methods to remain responsive and emergent. Finally, we discuss the complexities of studying the assemblage of humans, walking, pain, and landscape, illuminating the transformative potential of posthuman frameworks in understanding lived experiences of pain.
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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 | 0.067 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.071 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".