Bibliographic record
Abstract
A common visual motif in Roman funerary art is the figure of Alcestis, a ueen ho sacrificed herself in order to save her husband s life.The myth, fully extant in Euripides third-century bce play Alcestis, tells the tale of Alcestis, the beautiful daughter of King elias and the ife of King Admetus. 1 hen Admetus commits a sacrificial mista e re uiring the ultimate retribution his death his ife Alcestis, a paradigm of ifely fidelity, o ers to die in his place.She is rescued from the afterlife by ercules and reunited ith Admetus.The myth is visualized in a number of ays: ercules appears leading Alcestis out of the under orld in some examples Alcestis appears reuniting ith Admetus in others.Some images depict her in the under orld, hile others depict her death.The imagery associated ith the myth of Alcestis in all painting lac s a comprehensive investigation.This article attempts to remedy the situation, providing both an analysis of the imagery on all paintings and an interpretive frame or that could be applied to other funerary images.This article suggests that the interpretive frame or must ta e into account the physical position and context of the all painting.sing images of Alcestis as a case study, this article ill suggest that motifs depicting Alcestis ere multivalent images that could be interpreted in a number of ays depending on the physical context in hich it as displayed.I ill argue that bet een the second and fourth centuries ce, the standardized visual representations of the Alcestis myth in all painting transformed from motifs decorating large columbaria that focus on the transition bet een the earthly and I ould li e to than Dr. Bjorn E ald for his comments on the paper.Initial funding for this paper as made possible by a SS RC CGS scholarship (2013-2016). 1 See J.E. Thorburn, The Alcestis of Euripides (Le iston, ..: Ed
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".