Humanitarian Innovations and Material Returns: Valuation, Bio-financialisation and Radical Politics
Bibliographic record
Abstract
This article critically examines the global humanitarian innovation movement by conjuncting it with the stem cell biotech sector to trace how in the assemblage of matter and code conflicts emerge about notions of suffering, pain, enhancement as well as markets that alter the very material forms of life and economy. In the first section, I look at two things simultaneously: a bio-humanitarian project—the Cypriot search for and DNA identification of the post-war missing—and clinical trials performed by the biotech corporate sector. I trace their respective methods of value and valuation as not only dependent social molecuralised practices but also as translation technologies of kinship, creation of new notions of life and death and governance. In the second section, I take a close look at the emergence of humanitarian and clinical labour as a global assemblage to show how humanitarian organisations and transnational corporations orient themselves towards certain labour assemblages in the search ‘anywhere’ to learn about, borrow and translate technologies supporting the ‘business’ of empire. I finish with broader theoretical implications of the humanitarian work post war and the clinical labour of patients in stem cell therapies.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.101 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| 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".