Bibliographic record
Abstract
To judge from Greek literary sources, to be wounded in the back was shameful, for it connoted that the victim had failed to stand firm in battle and turned to flight. It is of interest, therefore, to survey the numerous representations of battle on Attic red-figure vases to see whether this negative aspect is manifest in the visual realm. It transpires that there is no consistent effort by Athenian vase-painters to imbue back wounds with a pejorative or negative aspect, even when a clearly identifiable enemy is depicted. Much more striking, however, is a clear distinction between deaths on and off the battlefield, and it is with the latter that we may observe a negative presentation of death. À en juger par les sources littéraires grecques, le fait d’être blessé au dos était honteux, parce qu’il révélait que la victime avait failli à rester ferme dans la bataille et qu’elle s’était retournée pour fuir. Cependant, il est intéressant de parcourir les nombreuses représentations de batailles sur les vases attiques à figure rouge, pour voir si cet aspect négatif est manifeste dans le domaine visuel. Il apparaît qu’il n’y a pas d’effort conséquent de la part des peintres athéniens pour associer les blessures au dos avec un aspect péjoratif ou négatif, et ce, même si un ennemi clairement identifiable est représenté. Beaucoup plus frappante, en fait, est la claire distinction entre les morts sur et hors du champ de bataille; c’est avec ces derniers que nous pouvons observer une présentation négative de la mort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".