Classical Cybernetics and Transhumanism: A Reply to Richmond’s Review of <i>The Nature of the Machine and the Collapse of Cybernetics</i>
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".