Bibliographic record
Abstract
Heroes are the first paradeigmata, for everyone, even for philosophers. Their most important virtue is tlemosyne, i.e. the ability to choose and resist in the face of dangers and difficulties. But the will has some more problems. Heroes have to deal with the gods and with Fate, that is, with the Moirai and their own destiny. Heracles, the greatest of all heroes, performs the well known labours in the service of Eurystheus, by the will of the gods. Aegisthus, Clytemestra’s lover and Agamemnon’s killer, is the negative model: he indeed chose what he wanted, but then he becomes the model of hybris. Agamemnon is the commander-in-chief of the Achaeans. But in the events of the war, he reveals all his errors of inadequate will. Achilles, the strongest warrior of the Trojan War, chooses between life and death and then regrets his choice when it is too late. Odysseus, the hero of metis, is perhaps the only one who knows how to choose the paths of the will. Certainly, with many risks and suffering. But this is the virtue of tlemosyne.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".