Digital Rock Art: beyond 'pretty pictures'
Bibliographic record
Abstract
The term 'Rock Art' is loosely used in this article to refer to prehistoric carvings and paintings. Rock art research has changed profoundly in the last two decades. Partly, this is due to the introduction of more 'scientific' methodologies such as digital recording, to overcome the subjective nature of analogue documentation methods. Digital recording offers not only 'pretty pictures' but more immediate and quantifiable datasets and methods of analysis. As a result, new research implementing complex, multi-scalar and inter-relational analyses, which do not focus solely on the motifs or the landscape location, but encompass many variables of the rock art assemblages, have been successful in bringing rock art to wider narratives of prehistory. This article reflects on the interaction between rock art and digital archaeology, considering how the application of digital resources has changed the way we think, record and conduct research in this field. It will be illustrated by two main case studies from Iberia: Schematic Art in its painted form, and Atlantic Rock Art, a carving tradition.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".