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Record W4211135865 · doi:10.1080/20961790.2021.2002524

Gaining Community Entry with Survivors for Forensic Human Rights and Humanitarian Intervention

2022· article· en· W4211135865 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueForensic Sciences Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCambodian History and Society
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesWenner-Gren FoundationNational Science Foundation
KeywordsGrassrootsHarmIntervention (counseling)Human rightsForensic scienceSociologyForensic anthropologyPolitical scienceCriminologyPublic relationsEngineering ethicsLawAnthropologyGeographyEngineeringMedicinePoliticsNursing

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0300.005
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.175
GPT teacher head0.428
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it