Bibliographic record
Abstract
Abstract This article is a critical interdisciplinary study of biohacking as a specific case of transhumanism and its goals of enhancement and age intervention. It focuses on the organising principles underlying the biohacking movement's relationship to ageing and technoscience. The argument traces how the historical and scientific body technologies of molecularisation, functional age, optimisation, and quantification made possible the biohacking vision of the ageing body as amenable to modification, enhancement and improvement beyond its natural limits. Conclusions consider the wider implications of biohacking by pointing out four important issues that frame our cultural ambivalence about ageing: the tension between biohacking's supposedly liberating enhancement technologies and their obeisance to a tyranny of self‐disciplinary practices and the authority of bio‐data; the social meaning of biohacking hierarchies of human value, based on modifiable fitness and enhanceable performance; the implications of the biohacking program for gendered ageism; and the ethical limits of biohacking, not only in terms of potential harms to a person but what it can mean to exceed the natural limits of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".