On The Verge of the Hybrid Mind
Bibliographic record
Abstract
Recent advances in neurotechnology allow for an increasingly tight integration of the human brain and mind with artificial cognitive systems, blending persons with technologies and creating an assemblage that we call a hybrid mind. In some ways the mind has always been a hybrid, emerging from the interaction of biology, culture (including technological artifacts) and the natural environment. However, with the emergence of neurotechnologies enabling bidirectional flows of information between the brain and AI-enabled devices, integrated into mutually adaptive assemblages, we have arrived at a point where the specific examination of this new instantiation of the hybrid mind is essential. Among the critical questions raised by this development are the effects of these devices on the user’s perception of the self, and on the user’s experience of their own mental contents. Questions arise related to the boundaries of the mind and body and whether the hardware and software that are functionally integrated with the body and mind are to be viewed as parts of the person or separate artifacts subject to different legal treatment. Other questions relate to how to attribute responsibility for actions taken as a result of the operations of a hybrid mind, as well as how to settle questions of the privacy and security of information generated and retained within a hybrid mind.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".