Realism as a Representational Strategy in Depictions of Horses in Ancient Greek and Egyptian Art: How Purpose Influences Appearance
Bibliographic record
Abstract
When modern (Western) viewers look at ancient art, the first feature of the image that is often assessed is its relationship to ‘reality’. How ‘real’ the image looks is inextricably linked to its evaluation and therefore the viewer’s estimation of its quality. The more ‘realistic’ an image is deemed, the more it is appreciated for its historic and aesthetic value. This fixation on reality has often affected the assessment of ancient imagery. It can create a bias that limits the researcher’s ability to analyse and interpret the image(s) to their full potential. When studying ancient images, the viewer should always keep in mind its original purpose. Rather than looking for reality through the notion of resemblance, the degree of reality should instead be assessed through the way the subject is being conveyed as the image’s purpose dictates its appearance. This article will use depictions of the horse in ancient Egyptian and Greek art to highlight some of the challenges one encounters when studying ancient images’ relationship with reality. It will show why it is important for scholars to focus on the image/object’s purpose, their resemblance to their subject, and their meaning in terms of the message(s) they are meant to convey.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".