Hybrid Corporeality and the Multiplicity of Human Death: A Post-Anthrocentric Perspective
Bibliographic record
Abstract
Rapid development of cognitive and neurosciences undermined the Cartesian view on the human body as a bounded and autonomous entity. A plethora of publications on enhanced memory, external cognition, extended mind, embodied self, or distributed corporeality confirms the view that the human body and mind are not self-contained entities, producing the world as a prosthetic set of “extensions” or parts of hybrid wholes, which we interpret as cyborganic assemblages. However, in this abundance of documented entanglements of bodies and minds with their surrounding settings, of fusions of corporeality with inert matter, there is scarce, if any, reflection on the posthumous fate of these hybrids and on the multiple forms of their deterioration, that establish what the author provisionally describes as multiplicity of human death. The paper presents the analysis of various forms of human body and inanimate matter integration and their posthumous persistence or deterioration. The view on the human body as multiple provides corollary of its death as a multimodal, manifold set of events, distinguishing biological, lived, and social bodies and their heterochronous deaths. The heterochronicity of human death is illustrated with the description of private commemorative practices that form a geography, distinct from the usual public commemoration places.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.051 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".