Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Minds enable people to perceive, imagine, solve problems, understand, learn, speak, reason, create, and be emotional and conscious. Competing explanations of how the mind works have identified it as soul, computer, brain, dynamical system, or social construction. This book explains minds in terms of interacting mechanisms operating at multiple levels, including the social, mental, neural, and molecular. Brain–Mind presents a unified, brain-based theory of cognition and emotion with applications to the most complex kinds of thinking, right up to consciousness and creativity. Unification comes from systematic application of Chris Eliasmith’s powerful new Semantic Pointer Architecture, a highly original synthesis of neural network and symbolic ideas about how the mind works. The book shows the relevance of semantic pointers to a full range of important kinds of mental representations, from sensations and imagery to concepts, rules, analogies, and emotions. Neural mechanisms are used to explain many phenomena concerning consciousness, action, intention, language, creativity, and the self. This book belongs to a trio that includes Mind–Society: From Brains to Social Sciences and Professions and Natural Philosophy: From Social Brains to Knowledge, Reality, Morality, and Beauty. They can be read independently, but together they make up a Treatise on Mind and Society that provides a unified and comprehensive treatment of the cognitive sciences, social sciences, professions, and humanities.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.231 | 0.071 |
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 it