Arkeologi, urfolk och rätten : En studie av relationen mellan arkeologi, arkeologer, urfolk och rättsprocesser i Sverige och Kanada
Bibliographic record
Abstract
Archaeological evidence has become an important part of the argument for the Indigenous peoples of several countries in legal proceedings concerning their rights. This thesis aims to explore how archaeologists and archaeological research are affected by acting as expert witnesses or being used as evidence in these proceedings. Another aim is to explore the differences and similarities between Sweden and Canada in these matters. The main material consists of interviews with seven archaeologists, four Swedish and three Canadian, whose research in various ways have been involved in legal proceedings concerning the rights of Indigenous peoples: The Sámi in Sweden and the Indigenous peoples of Canada. The analysis of the interviews is based on seven themes: awareness, impact, responsibility, experience, objectivity, archaeology and law and consequences. The result shows several things. It shows that the issue of archaeology in legal proceedings is a sensitive matter, and that the archaeologists have somewhat ambivalent feelings about it. It also shows that the involvement of archaeologists and archaeological evidence in these legal proceedings raises discussions about ethics, objectivity, and reputation. One conclusion to be drawn is that there is need for more open discussion and education on the subject.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".