"Measure me in metered lines": unreliable narration and the hermeneutics of narrative identity in contemporary 'Indie' song lyrics
Bibliographic record
Abstract
In the interest of exploring the hermeneutics of narrative identity in popular song lyrics, that is, the textual process by which a song's narrator imparts their story to the listener, this thesis examines a specific generic and temporal group of 'narrative' song lyrics against the branch of narrative theory relating to unreliable narration. Theories of unreliable narration have been selected as this study's key theoretical framework both because the area is central to contemporary literary studies (Nünning 2005: 2), and because, like song lyrics themselves, theories of unreliable narration problematize the notion of a unidirectional flow of meaning from the speaker/implied author to the reader/listener. Thoughtful, well-researched, highly literate (and often literary) lyrics are a central facet of the "indie music" genre's aesthetic. In this thesis I will therefore primarily focus on two of the indie music scene's most critically lauded lyricists, Colin Meloy of American indie-prog-rock band The Decemberists and John K. Samson of Canadian indie-folk-punk band The Weakerthans. Both Meloy and Samson's lyrics, I suggest, not only withstand such close critical scrutiny, but actually invite it. Demanding (and, arguably, enforcing) a new contract with the listener, Meloy and Samson's lyrics are representative of a larger shift in what an 'ideal' audience looks like in indie music, from casual listeners to an active (Schafer; Nancy), practiced, and critical audience. Engaging with a range of literary theoretical and musicological texts, the broad intentions of this project are to explore the formal complexities of first-person narration in contemporary indie song lyrics and to simultaneously diversify the potential scope for the application of theories of unreliable narration.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".