An analysis of victim lifestyle in assessment of victimization of Native-American women
Bibliographic record
Abstract
AN ABSTRACT OF THE THESIS OF JOSEPH PIERRE KEENE, for the Master of Arts degree in Administration of Justice, presented on June 9, 2009, at Southern Illinois University Carbondale. TITLE: An Analysis of Victim Lifestyle in Assessment of Victimization of Native-American Women MAJOR PROFESSOR: Dr. George Burruss Native-American women have endured victimization for five centuries. The problem of Native-American female victimization should be a topic of great concern but has not been studied very well. Dugan & Apel (2003) demonstrated that a young unmarried woman, frequently transient, living in an rural setting, having children under the age of 12, and going out every night predisposed Native-American women to violent victimization because "risk" factors were heightened and "protective" factors were jeopardized. However, this theoretical approach involved use of routine activities theory to help explain the situational context of Native-American female victimization, which possibly suggested victim blaming. Therefore, the use of lifestyle theory vs. analyzing "risk" and "protective" factors coinciding with routine activities theory was used to help explore the nature and extent of Native-American female victimization. This study used NCVS data from 2005 (n = 4252 cases; Caucasian (n = 2987), African-American (n = 522), American Indian (n = 104), Asian (n = 91), Hispanic (n = 541), Other (n = 7)) to explore the nature and extent of Native-American female victimization (U.S. Department of Justice, 2007). This analysis contributed to relevant literature in regards to Native-American female victimization by examining contributing factors that were linked to Native-American female victimization, and it also enhanced previous literature establishing the predicating factors that precipitated disproportionate statistical findings of Native-American women having the highest percentages of victimization of any race of woman in the U.S. Findings indicated that higher rates of victimization took place off tribal land more so than on tribal land for Native-American women, contrary to previous literature findings that Native-American female victims encountered higher incidents of victimization on reservations as opposed to non-reservation land (due to lack of prosecution, jurisdictional issues) (Amnesty International, 2007). Further research is needed to explore the lack of prosecution of crimes and conflicts of interest between U.S. and tribal laws in regards to their impact on the victimization of Native-American women. Furthermore, findings of Native-American women having the highest percentages of victimization of any race of woman in the U.S. have prompted further research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".