The Neuro-Complex: Some Comments and Convergences
Bibliographic record
Abstract
In this short think-piece we trace the newly emerging and rapidly expanding dimensions and dynamics of the “neuro-complex.” What this amounts to, we suggest, are a series of bio or neuro “convergences” of sorts regarding the brain and mental worlds, which in turn are traceable through what we term the bio-psych, pharma-psych, subjectivity-selves, wellness-enhancement, and the neuroculture-neurofutures relational nexuses. These issues are then illustrated through two brief case studies regarding brain scanning technologies and the problems and prospects of cognitive enhancement. The paper concludes with some final reflections on these matters and a call for further research in this rich and challenging domain as the neuro-complex continues to expand in expected and unexpected, yet equally rich and fascinating, ways.
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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.014 | 0.035 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".