Bibliographic record
Abstract
It is well known that in early Greek epic old age was something that could be scraped off a man, and it is the purpose of this note to explore the image and to suggest a possible origin. The idea is first attested in a counterfactual conditional sentence in Phoenix's speech atIl.9.445–6: ‘nor even if [a god] himself were to undertake to render me young and flourishing after scraping off old age …’ (οὐδ' εἴ κέν μοι ὑποσταίη αὐτός | γῆρας ἀποξύσας θήσειν νέον ἡβώοντα …); in a description of Medea's magical rejuvenation of Aeson in theNostoi(fr. 7.2 Bernabé = 6.2 Davies, γῆρας ἀποξύσας); and in the account of Eos' botched attempt to make Tithonus immortal in theHomeric Hymn to Aphrodite(223–4): οὐδ' ἐνόησε μετὰ ϕρεσὶ πότνια Ἠώς ἥβην αἰτῆσαι, ξῦσαί τ' ἄπο γῆρας ὀλοιόν. Nor did lady Dawn think in her mind to ask for youth and to scrape off ruinous old age. This language also occurs in Late Antiquity, and Andrew Faulkner in his commentary on theHymn(ad loc.) cites Greg. Nanz.Carm.1.2.2.483 (PG37.616) in the fourth centurya.d.and the much later Cometas,Anth. Pal.15.37.2. So far as the imagery implicit in these passages is concerned, S. D. Olson writes, ‘old age is imagined as a scurf or patina that can be scoured off a person’. Faulkner, however, prefers a more precise reference: ‘Griffin … relates it to the idea of old age as a skin that can be shed, as that of a snake’, and he helpfully cites Theodoros Prodromos,Carmina historica24.18, ἀπόξυσαι τὸ γῆρας ὥσπερ ὄϕις (‘to scrape off old age like a snake’).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".