Bibliographic record
Abstract
This screenplay is an adaptation based on the mythological inventor, Daedalus. Most audiences are less familiar with who Daedalus is compared to what he accomplished (designed and built the Labyrinth) or who he is associated with (his son, Icarus). For the most part, as found in Apollodorus’ The Library, Daedalus’ story weaves throughout the background of better-known heroes and villains. That is not to say his story is any less interesting, or – as many myths end – tragic. In my script, I attempt to flesh out the brief origins of the great inventor. This tale is found in The Library and claims that Daedalus once lived in Athens, taking his nephew Talus as his apprentice. When Daedalus realizes that Talus could potentially rival his own craftsmanship, the inventor tosses the boy off of a building in a fit of blind rage. It is a tragic tale in itself, but with the script, it is my goal to reshape the narrative so that it might appeal to a contemporary audience while staying true to the general themes of tragedy and playing them up in a grander scale. As I do so, I include some better-known characters in mythological history who chronologically (in the loosest sense of the word) could have interacted with Daedalus in Athens. My screenplay toys with the tragedy genre as Daedalus, his sister Perdix, and his nephew Talus are slowly sucked into the influence of a famed sorceress. The choice to include Medea (and later on, Jason) is based on Euripides’ renowned play, Medea, in which Aegeus (king of Athens) offers the magic user refuge in his city. Later, in the Theseus myth, we learn that Medea has married Aegeus (after killing her children to get back at Jason and fleeing from Corinth). Not only does Medea’s tragedy intersect with Daedalus’, but it also provides me the opportunity to have these strong characters interact while still hitting all the plot points found in the original myth. My goal while writing this screenplay was never to repeat the same story. Instead, it was to revise the rhythm of the tragedy while still maintaining the important beats that had rung so true to me in the beginning. In this attempt, I have shifted the definition of tragedy to match that of today’s general public, just as Shakespeare did in his day-and-age. In my critical essay, following the script, I explore this idea of changing ideas behind the definition of tragedy. I also explore how the experiences of cinematic spectacle affect the tragic genre as opposed to the theatrical.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.220 | 0.062 |
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".