Bibliographic record
Abstract
Emma Gee takes up an ambitious task: an explanation of the lasting importance of the Phaenomena of Aratus, who transferred into Homeric verse a fourth century astronomical prose treatise of the same name by Eudoxus of Cnidus in the 270's bc.Fusing traditions of technical astronomy and meteorology with the didactic poetry of the Archaic poet Hesiod, the Phaenomena was an immediate classic and remained widely read and imitated for centuries to come.Although the poem has begun to receive more attention from scholars in the past 50 years, 1 a general study of its reception has yet to emerge.Gee seeks to fill this void by inserting the Phaenomena into a larger tradition of astronomical thought spanning the seven centuries between Plato and the Roman emperor Julian.Although Aratus' importance as a poet generally goes unchallenged, Gee's is the broadest treatment of the Phaenomena and its translations by Cicero, Germanicus Caesar, and Avienus as an astronomical tradition referenced at length by several important Latin poets.In the end, the success of the arguments relies on an intricate array of detailed, close readings of text, which compel to varying degrees.Even where these arguments fail to be completely convincing, versions of Gee's theses nonetheless remain plausible.Aratus and the Astronomical Tradition constitutes a large step in the general study of Aratus' ancient reception.In what follows, I will summarize and describe the arguments of each chapter, commenting on Gee's argumentative strategy along the way.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".