Locked In. 2007. By Mike Esposito. Published by Durban House. 430 pages. Price C$16.
Bibliographic record
Abstract
and small dogs, setting Vladimir Putin as an example.Then it is back to Darwinism with the rather obscure title "Evolutionary Setups for Off-Line Planning of Coherent Stages" and a treatise by William Calvin which explains the title.Perhaps "think before you throw" would cover it more concisely.Creativity is clearly and instructively analyzed by the philosophical paper of Steven Harnad.Pasteur's dictum of "chance favours the prepared mind" could be modified: chance favours the prepared open mind.Eran Zaidel and Jonas Kaplan invite you to participate in their "flashy" web-based experiments on callosal interhemispheric transfer, investigating alexithymia (the inability to express emotions by patients with callosal or right hemispheric damage).Ray Gibbs cleverly poses the age old mind-body question in a dramatic form of a conversation between a student and a prof.He takes you (and Molly the student) from mirror neurons to metaphors.Sid Segalowitz leads us onto the dangerous path of reductionistic neuroscience and determinism but makes a valiant effort to rescue free will.J. Panksepp tells us that he is 'wed to the idea perhaps beyond reason that affect is the central compass of life well lived".Appletree Rodden's chapter on humor seriously lights up the whole brain (wait till you read his bio-sketch) and McCormick follows Hans Selye into the well trod field of the neuroendocrinology of stress.I liked the smell of gasoline when I was a kid, so I read with sympathy mixed with horror the chapter on petrol sniffing, sorcery and aboriginals.The best is left to the last: Noam Chomsky, answering questions posed to him by contributors to the book.The Olympian answers are complex and obscure and at times a surprising "I don't know".Regardless of Chomsky's politics, or quite possibly because of it, when he speaks about language, people listen.Each chapter is prefaced by the editors in a paragraph of a few enticing sentences.I read these after reviewing the book, so I could not be accused of lifting descriptive elements and bypassing the content.The book seems to be aimed at the general neuroscience audience without requiring any specialized knowledge.It is uniquely posed between a technical and a popular science volume and is a highly readable, entertaining and instructive one.Henri Cohen and Brigitte Stemmer, along with their contributors must be congratulated in accomplishing their aim.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.912 | 0.907 |
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".