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Record W4367180869 · doi:10.1080/10253866.2023.2206128

Bodies as machines. Machines as bodies

2023· article· en· W4367180869 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueConsumption Markets & Culture · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsYork University
Fundersnot available
KeywordsPersonhoodDehumanizationMetaphorMeaning (existential)PosthumanismHuman bodyTranshumanismEpistemologySociologyCognitive scienceHuman enhancementAestheticsComputer scienceArtificial intelligencePsychologyPhilosophy

Abstract

fetched live from OpenAlex

From early Greek philosophers to Descartes’ machinic metaphors of humans-as-machines, to the emergence of physical machine-like humans, the intersections of the human body with machines circle back through hundreds of years of debates on human-technology relationships. We live in an age when robots are becoming increasingly human-like with artificial intelligence that mimics and sometimes exceeds our own. At the same time, we humans are adopting cyborg-like modifications to improve ourselves through biological, mechanical, and computer technologies. This conceptual paper presents a historical overview of the human-machine merger as both a metaphor and material reality. We show that the body has no intrinsic meaning for its distinct social constructions in technophilic and bioconservativist perspectives. This leads to a critical need for discussions about the issues related to dehumanization and personhood. These two topics must inform future research efforts to explore a future when current concepts of humanness may not hold anymore.

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.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.008

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 head0.071
GPT teacher head0.358
Teacher spread0.287 · 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