Recognizing Injustice, Reclaiming Voices: Establishing a Missing Persons Database for Missing and Murdered Indigenous Women and Children in Nebraska
Bibliographic record
Abstract
Indigenous people have been the victims of harsh persecution since the time of colonization; this has negatively impacted Native communities and has contributed significantly to the ongoing crisis of Missing and Murdered Indigenous Women and Children (MMIWC) across the United States and Canada. Under Legislative Bill 154, Richards et al. (2021) conducted an initial study to identify missing Native cases in Nebraska and in 2023, the Attorney General appointed a Missing and Murdered Indigenous People (MMIP) Liaison to help combat the number of missing Indigenous in the state. This thesis contributes to previous research by analyzing 20,049 reports from three missing persons databases for Nebraska from 2015 to 2023, including Lincoln Police Department’s missing person reports, the Nebraska State Patrol’s Missing Persons Clearinghouse, and NamUs. At the same time, population statistics are utilized to compare average populations in the state to their representation in missing person reports. This research found that Native women and children are overrepresented in two of the three missing persons databases, while Native men are overrepresented in one. Due to the high number of missing Native reports, this research also contributes to the conversation surrounding the MMIWC crisis by suggesting a list of best practices for law enforcement, government entities, tribal entities, and community outreach programs so that risk factors for such cases may be identified sooner. The results also suggest the creation of a missing person database for MMIWC cases by highlighting the large scale of missing person reports in Nebraska. Advisor: William R. Belcher
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".