Bibliographic record
Abstract
Over the past ten or so years, brain plasticity has become an extremely hot scientific trend and a huge commercial enterprise. From the parent who wants to give his or her newborn an enriched environment to promote superior brain growth to the aging adult who wants to stave off Alzheimer's disease, exercising, enriching, and training the brain has become a multimillion-dollar industry. Hundreds of brain promotion companies have sprouted up, such as The Baby Einstein Company, LLC, and hundreds of new books are published each year on brain enrichment. “Brain health,” “brain training,” and “brain fitness” are terms that are bandied about in the advertising world, suggestive of the possibility of improving and prolonging intellectual health. However, this “brain improvement” commercialism, although occasionally overstated, is not without some foundation in hard science: the discovery of brain plasticity. The roots of the concept of “brain plasticity” can be traced toWilliam James's seminal work, The Principles of Psychology (1890), in which he clearly understood that behavior, habits, or instincts are governed by certain physiological limitations. He states, “Plasticity, … in the wide sense of the word, means the possession of a structure weak enough to yield to an influence, but strong enough not to yield all at once.… Organic matter, especially nervous tissue, seems endowed with a very extraordinary degree of plasticity of this sort; so that we may without hesitation lay down as our first proposition the following, that the phenomena of habit in living beings are due to the plasticity of the organic materials of which their bodies are composed” (p. 106).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".