Bibliographic record
Abstract
Handbook of Functional Neuroimaging of Cognition, Second Edition. 2006. Roberto Cabeza and Alan Kingstone (Eds.), Cambridge, MA, The MIT Press, 480 pp., $65.00 (HB) The first edition of the Handbook of Functional Neuroimaging of Cognition, edited by Roberto Cabeza and Alan Kingstone, was a welcome addition to the cognitive neuroscience field when it was published in 2001. There were chapters on the history of neuroimaging and analysis, and all the major cognitive areas that had been studied at the time, written by senior people in their respective areas. That a second edition has appeared so soon after the first is a testament to the rapid growth of the cognitive neuroscience field, which is both gratifying and somewhat daunting to those of us who vainly attempt to keep up with this burgeoning literature. The same authors as in the previous edition write some chapters, but many have been penned by different authors, equally well known in the field, which is also a sign that the field is healthy and growing.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 0.014 |
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".