Bibliographic record
Abstract
Rapa Nui has its own night noises. There is always music, just a throb of it in the distance. There are voices and laughter rising above the pounding surf. My first encounter with the music of Easter Island (or Rapa Nui) happened half a world away, in a Chilean migrant community festival in outer suburban Melbourne, Australia. In the late 1960s, a change in immigration policy enabled the first wave of migration of Chilean workers to Australia. They were soon followed by thousands of political refugees fleeing the Pinochet dictatorship (1973–1991). These groups mainly settled in Sydney and Melbourne, where they often converged with other recently arrived Latin American migrants but also maintained independent community events. Some Chilean migrants were prominent in Melbourne’s live music scene, especially in Latin dance bands, which experienced a surge in popularity from the Gypsy Kings–inspired “world music” boom of the late 1980s and early 1990s. As a trombonist trying to find my feet in the freelance gig economy, I found that the horn sections of these bands provided a ready source of income. I was also fortunate to have some contacts in the community, having grown up and gone to school in one of the areas where Chilean refugees and their children had settled. Overall, I spent almost a decade in and out of Latin bands in Melbourne, hanging out with Latin American musicians and later researching their contribution to Australian popular music (see Bendrups 2011b; Garrido and Bendrups 2013).
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.362 | 0.243 |
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".