Bibliographic record
Abstract
This article builds on Kozak’s 2016 monograph, Experiencing Hektor, which argued for using television narrative strategies to re-conceptualise ancient Greek oral epic. Inverting this dynamic, this article looks at how certain features of ancient oral epic can be useful in considering television’s narrative strategies, especially when it comes to repetitive narrative elements, from diverse forms of type-scenes to repeated phrases, character epithets, and longer formulae. The article also foregrounds the roadblocks for such an approach, from confusion over what constitutes a callback in both media, to considering the episode as a narrative unit, as epic episodes are not clearly delineated, and the season-drop continues to challenge the episode as a primary unit of narrative within contemporary television. Finally, the article points to several avenues of narrative analysis for both forms moving forward, urging scholars of Greek epics to think of narrative strategies beyond the constraints of oral composition, and urging television scholars to consider using the close-reading and televisual/textual analysis and data collection that remains central to classics as a discipline, but which are still primarily reserved for fans and popular media critics of television.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".