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Record W2902683547 · doi:10.1177/0048393118811308

Classical Cybernetics and Transhumanism: A Reply to Richmond’s Review of <i>The Nature of the Machine and the Collapse of Cybernetics</i>

2018· article· en· W2902683547 on OpenAlexaff
Alcibiades Malapi‐Nelson

Bibliographic record

VenuePhilosophy of the Social Sciences · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsHumber Polytechnic
Fundersnot available
KeywordsTranshumanismCyberneticsEpistemologyHumanityEnvironmental ethicsSociologyPosthumanPhilosophy

Abstract

fetched live from OpenAlex

Sheldon Richmond has written an insightful and exhaustive review of my book The Nature of the Machine and the Collapse of Cybernetics: A Transhumanist Lesson for Emerging Technologies (Palgrave Macmillan 2017). Richmond voices concerns regarding some suggestions I made about the future of humanity vis-à-vis a contemporary cybernetic reinstantiation in the form of Emerging Technologies. He suggests that future cybernetically rooted sciences (and the transhumanist technologies that come along with them) can pose peril for the human condition. This reply is intended to clarify certain points that Richmond brings up, by means of (a) responding to his suggestion that cybernetics and transhumanism could be independently understood, and (b) unveiling a metaphysical and ethical stance, shared by Richmond, critical to the observations I made regarding a “cybernetically organized mankind” made possible by Emerging Technologies. I identify Richmond’s position as (a) precautionary in nature, (b) for reasons perhaps more ethical than epistemological, somewhat out of sync with the cybernetic ethos.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.018
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.333
Teacher spread0.285 · 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.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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