Digital Heritage, Knowledge Networks, and Source Communities: Understanding Digital Objects in a Melanesian Society
Bibliographic record
Abstract
Abstract This article investigates digital heritage technologies from a Melanesian perspective. It explores—in the context of New Ireland, Papua New Guinea—the types of values placed on digital surrogates as a means to engage critically with recent debates on “digital” or “virtual” repatriation. It raises the question as to whether digital knowledge resources such as 3D digital objects are really seen as secondary or “second best” to the original or whether digital technologies reproduce, in new form, an economy of objects that sustains knowledge and revival practices. As a way to address this, the Mobile Museum pilot project was launched in January 2012 to help support the Nalik people of New Ireland reconnecting with and researching their cultural heritage in Queensland museums. This article demonstrates, in contrast to recent calls for an ideological return to the status of the museum object as put forward by Conn (2010), how ethnographic objects should be understood in terms of their performativity, mobility, and virtuality, which render them operative far beyond the physical realms of museum institutions. [digital heritage, digital repatriation, ethnographic collections, cultural revitalization, Melanesia]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".