Bibliographic record
Abstract
Beauty is truth, truth beauty, – that is all Ye know on earth, and all ye need to know. – John Keats When Amy Winehouse, a 27-year-old British singer, died in her London flat in July of 2011, few were surprised. While her musical talent was prodigious (her singing was compared to that of Billy Holiday), her battle with alcohol and drug addiction also attracted public attention. Her breakout song was entitled “Rehab.” Was she not another artist sacrificed at the altar of the muses? Are we not tripped, here, by a stereotype (Kaufman et al ., 2006), seduced by an availability heuristic? After all, how many alcoholics perish quietly out of the public eye? And given that we are unable to calculate conditional probabilities for this kind of case, we may wonder whether the chance of Amy Winehouse overdosing on alcohol would have been the same if she had not been an accomplished songwriter and singer (Silvia and Kaufman, 2010). An argument of the kind that can follow from assuming causal connections between artistic creativity and mental illness has been made by Currie (2011). He drew on a study by Post (1994) who made psychiatric diagnoses of 291 famous men from biographies. Post found that 48 percent of writers had severe psychopathology, whereas rates of psychopathology in scientists, statesmen, and thinkers were lower. On the basis of these and similar data, Currie argues that we cannot expect to learn anything useful from people who are as crazy as many writers seem to be. In this argument, Currie accepts that correlation means causality; that mental illness contributes to the writer’s art; and he assumes that the illnesses of the writers were permanent. Therefore, he concludes, one should not take the writers seriously. He also makes no comparison with levels of mental illness in the ordinary community.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".