Bibliographic record
Abstract
Abstract On 22 November 2017, the world of opera was shell-shocked by the death of the supergiant star Dmitry Hvorostovsky, a famous Russian baritone who died of cancer. His death was not a surprise because he had been terminally ill for a long time. However, the fact of his ‘sudden non-being’ in the world of opera appeared as a shock to all who loved it. My informants were no exception. Most of them had been lucky to attend his performances in Europe and to hear his unrivalled charming voice. ‘It feels like he is still here, still alive’, noted Zosya, ‘I think artists like him never die. They continue to shine even post mortem. Their glory is immortal. Will I ever reach this kind of fame and eternity?’ I did not respond to this question, which I guess was rhetorical—or maybe not. Maybe Zosya truly wanted to hear what a sociologist might think about her career potential. I softly switched the topic because I did not want to disappoint her. To be honest, I did not believe in her futuristic starry career, although miracles can, of course, happen. The reason I am so skeptical about Zosya’s future is that she and her idol Hvorostovsky are the two extra-polar types of global elite migrant.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.089 | 0.029 |
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".