Ruminations: Memoirs of a Psychiatrist from India. 2008. By Jagdish “Jack” Dang. Published by Trafford Publishing. 359 pages. Price C$37 approx.
Bibliographic record
Abstract
The paradigm shift from direct experimental approaches on brain function using the visual system in the initial chapters of the book to the more difficult areas of human behaviours involved in love, innovation and happiness can at times be difficult to follow.Although fMRI studies can suggest some sites of activation during the experiences of beauty and love the multiple other subtle inputs coming from memory engrams, environmental clues and expectations are very difficult to quantitate.The experience of artists, authors and musicians can open doors to the mysteries of human behaviours but they only give us a shadow of the neural networks that the brain can tap to decipher the macrocosm we inhabit.I enjoyed reading this book and appreciated the attempt of the author to bridge the expansive chasm between experimental result on visual sensory input and the intimate human experiences for which we all strive.The study of brain function and, in essence, the study of man has and will always have an element of mystery.This volume is an attempt to shed further light on the quest to uncover the fabric of this mystery and succeeds in opening the door a little bit further.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".