Bibliographic record
Abstract
It was some time after I first met Panamanian vocalist and multi-instrumentalist Lucho de Sedas, in early 2005, that he began playing a custom designed Fender Stratocaster whenever he performed for audiences in Toronto. In contrast to the sleek B. C. Rich Gunslinger Assassin that had followed him from Panama, Lucho’s new electric guitar had emblazoned on its front the tricolored Panamanian flag. This new guitar would become a key part of his signature look, for it communicated his connection to and love of country. Indeed, for the highly diverse contingent of Hispanic Canadians that made up the audience for his music in his newly adopted land, the instrument was also the most striking if not principal reference to the musician’s nationality. This is because—as Lucho would remark to me on several occasions—Panamanian music is little known outside of Panama. This lack of familiarity with the music to which he had devoted his life was deeply felt by Lucho, who is not only a household name in his own country, but had risen to fame performing the most widely embraced form of popular music in Panama....
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 | 0.000 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.358 | 0.162 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".