The cyborg, the ideology chip and the guru programme: the implications of cyborg technologies for the development of human consciousness
Bibliographic record
Abstract
There has been little thought given in science to the impact of direct brain‐machine interfacing upon the future development of human consciousness. Even less thought has been given to the possibilities for both optimizing and thwarting development in the cyborg child. A neurocognitive model of the evolution of cyborg consciousness is summarized, and from this model grounded speculations are offered pertaining to the future development of the higher cognitive functions in the cyborg child. It will be shown that cybernetic implants are “multistable”; that is, the artificial intelligence (AI) component of the cyborg brain‐machine linkage may function to condition development along ideological lines (the brain conditioned by the “ideological chip”), or may operate to open up neurocognitive development to new and heretofore unrealized limits (the brain’s development optimized by the “guru programme”). Development of the cyborg child may be conditioned in the interests of ideological concerns, or may lead to a consciousness that easily transcends all forms of ideology. Application of the guru programme may foster the emergence of new levels of cognitive complexity and information processing (à la Piagetian and neo‐Piagetian theory) that in turn allows new strategies of adaptation previously beyond human comprehension. The ethical and regulatory problems raised by cyborg technologies are addressed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".