Authenticity for the digitisation of Australian First Nations archaeology : case studies from Maiawali Country and Gunbalanya
Bibliographic record
Abstract
At its core, this thesis pursues an understanding of how authenticity and Walter Benjamin's experiential addition to authenticity, the aura, can be reproduced in digitised cultural heritage. The backdrop to this is an archaeological practice that is shifting towards ever greater integration of digital methods as tools of collection, analysis and dissemination. This digital shift largely postdated the intense theoretical debates of the 20th century and consequently remains poorly contextualised within the breadth of archaeological theory, with the rapid evolution of technology only making attempts at contextualisation more difficult. Considerable efforts have been made to mature the methods and discourse of digital archaeology, particularly in Europe, but these efforts, as with many others, reflect the archaeological and contemporary social context of their origin. In applying these overseas developments to Australian Archaeology, particularly the archaeology and cultural heritage of Indigenous Australians, there was a clear need for a locally appropriate adaptation. Consequently, the literature review of this thesis examines the ontological fluctuations in archaeology, cultural heritage standards and broader society to understand how authenticity has changed accordingly, as well as describing its contemporary pluralistic existence. This includes the review of the human-machine interface, digital archaeology projects, cultural heritage best practice and legislation alongside the possibilities of emerging technologies like generative artificial intelligence to ensure a holistic understanding of authenticity at the intersection of cultural heritage and digital technologies. Two case studies were undertaken to understand how the lessons learnt in the literature could be applied practically to Indigenous Australian cultural heritage settings. The case studies leveraged game development tools and novel hardware to produce media that reflects both the archaeological and cultural context of Australia's unique heritage. The flexibility of this approach allowed the varied digital data formats produced through digital archaeological practice, to occupy the same space, providing a more wholistic and engaging experience than what is typically available through specialist software. The methodology applied in this thesis incorporated CARE (Collective benefit, Authority to control, Responsibility and Ethics) practices alongside other examples of best practice to ensure that the Indigenous Knowledge, archaeological and cultural data was maintained and used with the informed consent of Traditional Owners and in a way which incorporated their interests and desires. [...]
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".