Bibliographic record
Abstract
This paper explores the ontological evolution of the use of ethnographic observations and data to improve our understanding of prehistoric rock art. In Europe, the use of ethnographic information in rock art research dates back to the early discoveries of prehistoric art at the end of the nineteenth century. But the value given to ethnographic observations today has changed significantly. The original use as a primary source for analogical reasoning based on a purely comparative approach (especially in the area of rock art interpretation) has proven to be unsuitable. Through the establishment of direct connections between cultures that were considered ‘similar,’ in spite of being distant in time and space, several reductionist interpretations of the meaning of European Paleolithic rock art were suggested (including totemism, hunting magic, or shamanism). These theories were also eventually applied to other rock art traditions in this, and other, parts of the world. This use of analogical reasoning is today met with scepticism. In this paper I present an argument for the continuing use of ethnographic data to interpret the rock art of past societies, provided that it is based on an ontological shift in the use of ethnographic analogies. Through a comparative analysis of European and Australian rock art traditions, I will discuss how a controlled use of analogies can be used to critically reflect and test archaeological interpretations in European rock art, to provide alternative interpretations, as well as to contribute to our understanding of the processes of creation and use of past rock art.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".