Bibliographic record
Abstract
<JATS1:p>Tom Waits's distinctive, bourbon-soaked growl, his unique persona, and his incorporation of musical styles from blues to experimental to vaudeville have secured for him a top-shelf cult following and an extraordinary critical respect. The idea of the Wanderer - someone who seeks an escape from all of life's problems, and dreams himself into oblivion - serves as the fundamental personality type around which all Waits's music revolves. Ten years of producing and touring with Waits's macabre folktale adaptation across Canada and the U.S. has given author Corinne Kessel direct access to his work, creative process, and his associates. In this comprehensive analysis, Kessel examines all of the many characters that have appeared throughout the course of Waits' musical career, fromClosing Time(1973) toOrphans: Brawlers, Bawlers, and Bastards.</JATS1:p> <JATS1:p>His raw form of expression and his evocative lyrics work together to form an emotional chronicle of society's misfits, outcasts, and lowlifes. He is not the sort of composer to chase after shiny red fire trucks to awesome blazing fires, but instead looks after the intangible dreams found dissipating in the last wisp of smoke from a cigarette, held in the weathered hands of a broken soul. Here, author Corinne Kessel pursues Waits into this distinctly murky and unsettled atmosphere to address in particular Waits's enduring questions of reality, landscape, and identity.</JATS1:p>
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".