“Invasive” and “Non-invasive” Technologies in Neuroscience Communication
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.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".