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Record W3211754368 · doi:10.32920/ryerson.14649084.v1

Hybrids of Mind and Body: The Forms and Freedoms of the Cyborg in Posthumanist Science Fiction

2021· preprint· en· W3211754368 on OpenAlexaff
Ben Berman Ghan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHumanityPhilosophyState of exceptionPosthumanPower (physics)RealmAbsolute powerOriginal sinEmbodied cognitionLiteratureArtLawAestheticsTheologyEpistemologyPoliticsPolitical science

Abstract

fetched live from OpenAlex

[Introduction] The Cyborg as a figure in popular culture – the body in a literal state of “human/machine symbiosis” (Katherine Hayles, How We Became Posthuman 112) – has sometimes been conceived as a monstrous figure, as a figure of otherness, a being whose status as a hybrid has placed them into the figure of what Giorgio Agamben might refer to as “the Homo Sacer, a person [who] is simply set outside human jurisdiction without being brought into the realm of divine law” (Agamben, Sovereign Power and Bare Life 82). The Homo Sacer, in other words, is a being who has been stripped of all recognition and humanity, deserving neither the rights of a human being or any other animal, and has come to be acknowledged only as an object. Agamben further defines the life of Homo Sacer’s exclusion as “unsacrificeability and [yet] is included in the community in the form of being able to be killed” (82), meaning that Homo Sacer can be killed, but that their killing would never constitute murder, as their life has no recognizable value.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.028
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.045
GPT teacher head0.312
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2021
Admission routes1
Has abstractyes

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Same topicNeuroethics, Human Enhancement, Biomedical InnovationsFrench-language works237,207