Bibliographic record
Abstract
As 3D scanning and photogrammetry are supplanting traditional illustration techniques with increasing speed, archaeologists and architectural historians have sounded alarms about what stands to be lost if hand drawing is altogether eliminated from fieldwork. This paper argues that the most direct threat is to a particular form of archaeological illustration which does not necessarily share the advantages attributed to other kinds of drawing. Recording by means of “technical drawing” communicates a collectively agreed interpretation of the ancient record, and its primary benefit is not stimulating creative thought but rather enhancing human observation. A review of two cases comparing the illustration of ancient Greek architecture through analogue and digital methods indicates that, in practice, both approaches draw attention away from the ancient subject and focus it on distracting protocols for the great majority of the time spent in the field. Even so, technical drawing requires protracted, in-person scrutiny of the subject, whereas 3D technologies pose a genuine risk of altogether eliminating meaningful human interpretation from the recording process. The greater efficiencies of digital techniques suggest a path forward, as time once allocated to tedious stages of technical drawing might be applied toward more thoughtful interpretive tasks. However, such measures must be deliberately integrated into a digital research program through planning around the very different cadences of the digital process.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".