From Scholarship to Practice: Standardizing Calls to Action in Neuroethics
Bibliographic record
Abstract
A significant goal of neuroethics is to offer neuroscientists, health care providers, law- and policy-makers and others, ways of thinking and acting on matters relevant to brain health and conditions that affect the central nervous system. This goal and related calls to action have been derived from theory or empirical work and bring different levels of normative force. To bring the latter in particular to the foreground of discussion, we explored for this Policy Forum different calls to action as they are associated with chosen terminology, the definitions of terms, origins to which they are benchmarked, locations in text, and targeted audiences. We find variability on all of these factors as they appear in the original foundational journals for neuroethics: AJOB Neuroscience and Neuroethics. We recommend that for a field whose very existence relies on uptake of advice, better consistency of language will improve credibility, acceptance, and implementation.
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 | 0.536 | 0.701 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.022 | 0.115 |
| Scholarly communication | 0.062 | 0.070 |
| Open science | 0.011 | 0.052 |
| Research integrity | 0.026 | 0.045 |
| Insufficient payload (model declined to judge) | 0.004 | 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".