Elevating neuroscience literacy and an approach for physiologists
Bibliographic record
Abstract
The field of neuroscience has made notable strides that have contributed to progress and change in a number of academic pursuits. However, the lack of understanding of basic neuroscience concepts among the general public is likely to hinder, and in some instances possibly even prevent, the appropriate application of scientific advancements to issues facing society today. Greater neuroscience literacy among the general public is necessary for the benefits of neuroscientific discovery to be fully realized. By actively enhancing neuroscience literacy, scientists can dispel falsehoods established by early research that harmed underrepresented communities, ensure that public conversations concerning neuroscience (e.g., legalization of psychotropic substances) revolve around facts, and empower individuals to make better health decisions. The widespread implementation of communication technologies and various forms of media indicate there are numerous means to engage classroom learners across disciplines and age cohorts and the public to increase neuroscience knowledge. Thus, it is not only necessary but timely that neuroscientists seek meaningful ways to bridge the widening knowledge gap with the public.
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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".