Emotional Truth. By Ronaldde Sousa. (Oxford UP, 2011. Pp. xviii + 391. Price £38.00.)
Bibliographic record
Abstract
This book is a collection of facts and arguments about emotion as a feature of our thinking and planning lives. Behind it I can make out a big picture about successful thinking, including appropriate feeling as a special case. In this review rather than describe all the facts and arguments I shall try to discuss the big picture which, I should say clearly, de Sousa never states in the terms I am using. So what follows puts things in a broader and cruder way than he does. As I read de Sousa, his central aim is to explain and defend a view about the fulfilled or authentic life, and to do so in terms of a linked set of comparisons between emotions and beliefs. The working out of this aim is tangled and often bewildering, as well as being clever and often very stimulating. The most ambitious idea, though, is just the thought that such a project is possible, that by asking what the analogues for emotion of truth, knowledge, and rationality are we can get some hold on, well, the meaning of life. Very few will be converted to wholehearted acceptance of the idea by this book, and some will dismiss it out of hand. But a good proportion of readers should be struck by a grudging admission: there's something important here.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.116 | 0.089 |
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".