Bibliographic record
Abstract
Umberto Eco’s understanding of postmodernism as the “ironic rethinking” of the past is one that insists – by definition – on a contextualist reading of cultural texts. In this sense, then, an understanding of the dialogical mutuality of texts and contexts – “postmodern” or otherwise – is inherent in Eco’s formulation, suggesting that any analysis of intertextuality must engage with issues not only of textuality but also of social and cultural history. In this paper, drawing on Eco’s insightful model, I propose an expanded understanding of musical intertextuality that moves beyond issues of appropriation and quotation, to examine not only the interrelationship of cultural texts but also the interaction of those texts with their socio-historical contexts: aspects of intertextuality which textually biased approaches inevitably fail to address. My analysis focuses on recordings of the popular standard “My Funny Valentine” by Frank Sinatra, Tony Bennett, and Miles Davis, addressing a range of both textual and contextual issues, and tracing the song back to its Broadway origins in Babes in Arms, the 1937 stage musical by the songwriting team of Richard Rodgers and Lorenz Hart. The analytical method is one that allows a broadly eclectic theoretical approach to the complexities of contemporary music and its canons, denying narrow interpretations of musical meaning and cultural value, and offering instead a suggestively intertextual reading of musical forms and practices. I argue that intertextuality needs to be understood as a fundamentally historical phenomenon, in which questions of meaning and value remain constantly in flux – revisited, reinterpreted, and reassessed as an understanding of the complex interrelationship of texts and contexts is broadened and deepened.
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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.057 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".