“Invasive” and “Non-invasive” Technologies in Neuroscience Communication
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
This paper analyzes a common distinction in neuroscience communication: the labels “invasive” and “non-invasive” attributed to brain-observation technologies. Because an implicit or explicit value judgment accompanies the term “non-invasive,” it has been used to promote technological progress, especially new brain-imaging techniques that have appeared in recent decades. This study’s material comes from interactions between some expert scientists and the political sphere. Expert reports on neuroscience from different advisory bodies in the French public sector have been collected and analyzed for use of the distinction between invasive and non-invasive. The paper shows that the meaning of these widely used labels varies according to the context, e.g., status of discourse, technologies compared, or stakeholders engaged in the discussion. The definition of what is understood as invasive or non-invasive becomes a strategic issue and can thus vary according to the methodologies favoured by experts participating in national advisory boards or councils.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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