Indigenous Advocacy and Gender Mainstreaming: Challenges and Recommendations for Women, Peace, and Security Practitioners
Bibliographic record
Abstract
abstract: Women, Peace, and Security (WPS) practitioners (including policymakers, scholars, and nonprofit leaders) in the U.S. and Canada have often focused their attention on the United Nations’ WPS initiative as a strategy for responding to conflicts abroad, particularly in the Global South. As a result of these limitations, black, Latino, and Indigenous advocates and peacebuilders in the U.S. and Canada remain largely unable to take advantage of WPS frameworks and resources. The subjectivity of the term “conflict” and the range of circumstances where it is used inspire this research. The selective application of the word “conflict” is itself a challenge to security, for conflicts can only be addressed once they are acknowledged and so named. Where does WPS intersect with contemporary Indigenous advocacy? A case study of the #noDAPL movement and the ways that nonviolence and women’s leadership emerged at Standing Rock, ND in 2016 provide a partial answer. Four challenges and recommendations are offered to WPS practitioners who seek to expand the availability of WPS resources to Indigenous peoples in the U.S. and Canada. These challenges and recommendations draw upon existing National Action Plans, legal and policy documents, and data from four interviews conducted with Indigenous women advocates in the U.S. and Canada in 2019. Above all, this paper seeks to encourage WPS practitioners to move beyond “gender mainstreaming” to consider not only how policies and practices impact women and men differently, but also how they may impact Indigenous people and settlers differently.
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.
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 | 0.067 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.036 | 0.047 |
| Scholarly communication | 0.036 | 0.034 |
| Open science | 0.009 | 0.032 |
| Research integrity | 0.021 | 0.029 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".