Unveiling the neurotechnology landscape. Scientific advancements innovations and major trends
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Scanning neurotechnology: what is being developed, where and by whom? Neurotechnology's developments hold profound implications for human identity, autonomy, privacy, behavior, and well-being, i.e. the very essence of what it means to be human. Since 2013, government investments in this field have exceeded $6 billion. Private investment has also seen significant growth, with annual funding experiencing a 22-fold increase from 2010 to 2020, reaching $7.3 billion and totaling $33.2 billion. This investment has translated into a 35-fold growth in neuroscience publications between 2000-2021 and 20-fold growth in innovations between 2000-2020, as proxied by patents. However, not all are poised to benefit from such developments, as big divides emerge. Over 80% of high-impact neuroscience publications are produced by only 10 countries, while 70% of countries contributed fewer than 10 such papers over the period considered. Similarly, six countries only hold 87% of IP5 neurotech patents. This report targets policy makers, researchers, patent analysists, scientists, technology enthusiasts, ethicists, and anyone interested in the intersection of neuroscience, technology, and society. This report sheds light on the neurotechnology ecosystem, that is, what is being developed, where and by whom, and informs about how neurotechnology interacts with other technological trajectories, especially Artificial Intelligence. The report underscores the need for evidence in support of policy making and calls for the ethical governance of neurotechnology, to ensure that its development and deployment respects human rights, fundamental freedoms and human dignity, safeguarding individuals and societies.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it