“Folks Don't Understand What It's Like to Be a Native Woman”: Framing Trauma via #MMIW
Bibliographic record
Abstract
Indigenous people are often part of the “fourth world”—a subaltern community whose members experience poor social outcomes despite living in a first world context. In North American, fourth world status has resulted in a “crisis” of missing and murdered indigenous women and girls. Activists have successfully garnered attention to the issue of “MMIW,” pressuring Canada to launch a National Inquiry and prompting US policymakers to introduce legislation. This study considered how Twitter has facilitated MMIW (cyber)activism by cultivating a collective indigenous identity. It considered how participants in #MMIW framed the nature of indigenous trauma in contrast to mass media framings of indigenous issues. The researchers conducted a thematic analysis of 481 tweets sampled from May-July of 2019. They found that hashtag participants framed indigenous trauma as (a) personal and pervasive, (b) systemic and structural, and (c) continued injustice, mobilizing a nation-building discourse.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".