“I Like to Keep my Archaeology Dead”. Alienation and Othering of the Past as an Ethical Problem
Bibliographic record
Abstract
As archaeologists, we have to deal with the dead, and as David Clarke once said, we like to keep our archaeology dead. From an epistemological perspective, alienation from the dead seems almost inevitable; otherwise, we would only project today’s conditions onto the past. Therefore, the past must be, and must remain, a foreign country. These alienating processes have ethical implications, however, especially when it comes to the study of human remains. In this article, we analyze the structures within the scientific discipline of archaeology that normalize practices, such as the labeling of human bone material during excavations and the object-like display of skeletons in museums. We argue that archaeologists have an – often rejected – ethical responsibility towards subjects from the past. We, therefore, seek to open up a debate concerning alternative strategies for the treatment of the dead.
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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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.095 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".