IN POSSESSION OF A STOLEN WEAPON: FROM JOHN GAY’S MACHEATH TO RUBÉN BLADES’ PEDRO NAVAJA
Bibliographic record
Abstract
This article analyses the cult salsa song “Pedro Navaja” by Panamanian artist Rubén Blades and the relevance of narrating such a genre peculiar to dancing, an analysis in accordance with adaptation theory outlined by Linda Hutcheon, and a Brechtian semiotic methodology which combinesGestus with theories of the sign. “Pedro Navaja” is arguably a re-writing of “Mack the Knife” – or Die Moritat von Mackie Messer – one which contains, as a sort of mise-en-abyme, elements not only of Kurt Weill’s song, but of both John Gay’s The Beggar’s Opera and Bertolt Brecht’s The Threepenny Opera. This study focuses on the fertile adaptation history of the highwayman Macheath since Gay’s work, on close-readings of “Pedro Navaja” – a narrativized cerebral salsa song – and on the ethics of adapting a character, story or genre. Finally, when the character Pedro Navaja is adapted from Blades’ song by a cineaste and playwright, Blades, furious with what others have done with ‘his’ character, decides to resuscitate his eponymous hero killed off at the end of the original song in order to regain the authority of his creation. From theoretical questions including how to read an adaptation or what constitutes an adaptation, this article focuses on the diachronic recurrences of a specific character-type, on the significance of juxtaposing particular historical junctures and on the violence of adaptation and authorship.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".