Deep-Time Images and the Challenges of Globalization
Bibliographic record
Abstract
Abstract In this collection of papers on globalization and rock art, we begin to examine how rock art research was historically shaped by a deep Eurocentric bias. We use the concept of deep time, following the recent focus of historians and other disciplines, where an appropriate scale of space and time is being explored to understand the human past (following McGrath and Jebb, Long history, deep time. Deepening histories of place. ANU Press, Canberra. https://doi.org/10.26530/OAPEN_578874 , 2015; Griffiths, Deep time dreaming: uncovering ancient Australia. Black Inc., Carlton, 2018). A focus on the “deep time story”, as Billy (Griffiths, Deep time dreaming: uncovering ancient Australia. Black Inc., Carlton, p. 5, 2018) asserts, reminds us that history is but one way of thinking about the relationships between past and present. Rock art research has multiple lenses, rather than being a universal science or all-knowing truth. Deeply engrained Eurocentric biases that drove the earliest research efforts into deep time art and its makers, has shifted to a more global perspective on rock art and the people who made it, by those who are involved in its research, and by those for whom it has multiple significances. The proliferation of rock art research in colonized parts of the world, particularly the USA, Australia and Africa, continues to call into question this Eurocentrism. This shift in focus has been fueled, in part, by globalization, which has resulted in many benefits for rock art researchers, including the expansion of inquiry into new territories and the rapid sharing of developments in new methods for surveying, recording and dating rock images. Globalization has also generated new challenges and tensions. There are still many countries and territories that are excluded from these discussions, and Western hegemony and patrimony as promoted by institutions such as UNESCO, often collide with the interests of nationalism and local communities. The chapters in this volume explore these tensions and many suggest strategies to promote more critical attitudes toward globalization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".