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Record W4214520564 · doi:10.15173/a.v2i2.2913

Body Hacking and Conceptions of Corporeality

2022· article· en· W4214520564 on OpenAlexaff
Morghen Jael

Bibliographic record

VenueAletheia · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHackerHuman bodyMateriality (auditing)InvisibilityMind–body problemDialecticSociologyEpistemologyComputer scienceAestheticsComputer securityPhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

This paper is the culmination of my independent research into the phenomenon of "body hacking": DIY techno-body modification that typically involves the surgical embedding of electronic or computing devices into the body. Body hackers are operating largely within a transhumanist vision (body hacing to improve and/or transcend the self or the human condition) and outside of traditional medical institutions. My paper addresses conceptions of the hacked body, including the deliberate and physical transition into "cybrog" and the blurred line of "humanness" that comes from a surgical merge with technology. I focus on the perspectives on corporeality that come from body hackers themselves; I find that there are varying opinions within the body hacking community on the value of the material body. Many body hackers aim to transcend or render obsolete the physical body - in search of a more enlightened or convenient existence, for example - but others see body hacking as a practice that makes them more human. Many body hackers see the physical human condition as a very limited one that is in need of improvement. My paper explores these perspectives and presents a nuanced picture of the modern body hacking philosophy, as a fascinating example of the interface between materiality/coroporeality and technology.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.085
GPT teacher head0.321
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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