Gaining Community Entry with Survivors for Forensic Human Rights and Humanitarian Intervention
Bibliographic record
Abstract
As forensic humanitarian and forensic human rights anthropology has continued to evolve, an ongoing concern in the field is meaningful engagement with survivors and the imperative to do no harm. For forensic anthropologists attempting to engage in grassroots forensic intervention, unaffiliated with an international investigation, means for effectively accessing and engaging communities has not been widely discussed. Here, forensic anthropologists draw on multiple, cross-cultural contexts to discuss methods and techniques for introducing forensic partnerships to communities. To do this, the scientist must consider their positionality as well as that of the stakeholders, develop effective local relationships, and consider a community-grounded approach. This paper argues that drawing on broader cultural anthropological training, ultimately informs one's ability to gain entry into at-risk and vulnerable communities while minimizing harm. To illustrate this point, examples are drawn from Canada, Uganda, Cyprus, and Somaliland.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".