Bibliographic record
Abstract
In both United States and Canadian mainstream news, the disproportionate rate of sexual violence against Native/American Indian, Alaska Native, and First Nations women is consistently underreported. Indeed, as Amnesty International concluded in April 2007 Ybanez , Vicki . Message #4177 . 12 April 2007 . [Google Scholar], despite centuries of brutalisation of Indigenous women, and even with the recent addition of its own one-hundred-thirteen pages of documentation of “many incidents of sexual violence against American Indian and Alaska native women[,]…the great majority of stories remain[s] untold.” At the same time, Indigenous women have been asserting their own stories around the trauma of violence in ways that work toward making whole what colonialism attempts to systematically dismember. One particularly powerful venue of re-membering the piecemeal and partial representations of violence against Native women in mainstream U.S. news is the U.S.-based internet community CAVNET_IW (Communities Against Violence Network – Indigenous Women), whose formation, intentions, and practices serve as a potent counter-narrative to the mainstream news media's consistent efforts to render Native women's stories invisible and insignificant. As a site that not only disseminates news about violence against Indigenous women but creates a forum for women to tell their own stories, CAVNET_IW has fashioned a potential safe space through which immediate and historical trauma affecting Native women paves pathways to personal healing and political change. At the same time, CAVNET_IW, a carefully monitored discussion space requiring member sign-up and message screening before posting, paradoxically works to end violence and heal trauma by necessarily circulating life stories that keep episodes of traumatic violence constantly present in the consciousness of CAVNET_IW members.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.014 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".