Bibliographic record
Abstract
In Tolkien studies, the character of Lúthien Tinúviel has frequently been de- scribed as central to the entire creation of the Eä (i.e. Middle-Earth) universe. As previous scholars have identified, the two major sources of her power are dance and song, which are sometimes described as extensions of her feminin- ity, though this is hotly debated and often outright rejected. Clare Moore, in her 2021 article “A song of greater power,” contends that J.R.R. Tolkien “in- creasingly [...] establishes Lúthien as a figure of power” as the story is written and re-written, wielding song and dance as expressions of self and influence (Moore, 2021, p. 7). Tolkien also “increase[es] her agency and autonomy,” and therefore “presents her as the foremost figure of his entire legendarium by establishing her influence over the history that comes after her,” and part of that power comes from song and dance (p. 7). Following Moore’s close reading work comparing the five major texts that tell the story of Lúthien Tinúviel, I create a corpus for use in R; look at term frequency; create dispersion plots to visualize patterns of occurrences across the various versions of the story; and calculate correlation scores between song variants and dance variants. These are all ways to identify the true sources of her power and their evolutions over the five key manuscripts: The tale of Tinúviel (1917), The Lay of Leithian (1925), Sketch of the mythology (1926), Quenta Noldorinwa (1930), and Quenta Silmarillion (1977). The dispersion plots particularly probe Moore’s argument that the art form of dance gives way to song over time. Lastly, I also consider what weaknesses these R tools bring to studying the Lúthien story, and make suggestions for a text analysis library for Tolkien studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".