Bibliographic record
Abstract
There are few books devoted to the topic of brain plasticity and behavior. Most previous works that cover topics related to brain plasticity do not include extensive discussions of behavior. The first to try to address the relationship between recovery from brain damage and changes in the brain that might support the recovery, this volume includes studies of humans as well as laboratory species, particularly rats. The subject matter identifies a consistent correlation between specific changes in the brain and behavioral recovery, as well as various factors such as sex and experience that influence this correlation in consistent ways. Evolving from a series of lectures given as the McEachran Lectures at the University of Alberta, this volume originally began as a summary of the lectures, but has expanded to include more background literature, allowing the reader to see the author's biases, assumptions, and hunches in a broader perspective. In writing this volume, the author had two goals in mind: * to initiate senior undergraduates or graduate psychology, biology, neuroscience or other interested students to the issues and questions regarding the nature of brain plasticity, and * to provide a monograph in the form of an extended summary of the work the author and his colleagues have done on brain plasticity and recovery of function.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.024 |
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".