Bibliographic record
Abstract
Much about this book will have a familiar ring, an impression launched by the cover image, which offers a very famous, ubiquitously reproduced nineteenth-century portrait (by Giovanni Boldini) of the composer. The series editor’s foreword, too, hovers on the verge of standard-view reiteration, bearing a likeness in more than one sense to the tone of any old early twentieth-century Verdi biography. The fourth volume in this Listener’s Companion series, Experiencing Verdi—like its three predecessors, dedicated to Stravinsky, Mozart, and jazz—aims in Gregg Akkerman’s words to ‘give readers a deeper understanding of pivotal musical genres and the creative work of their iconic practitioners’. This, it is claimed, is accomplished by ‘emphasizing throughout music as a lived listening experience’, thus ‘deepen[ing] for readers their enjoyment and appreciation’ of the musical works under scrutiny (p. ix). Within this framework, Akkerman thrusts Verdi into the limelight as a composer with a ‘masterful career and unique personal history’ (p. ix); a man whose ‘considerable gifts as a musician burst forth’ in spite of ‘the most terrible tragedies’ in his personal life; a figure who ‘transcended his own century’ with his music, and who deserves further attention owing to his political activism (p. x).
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.033 |
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".