Navigating across anthropological and Haudenosaunee knowledge: co-developing research using CBPR and Kaswenta (Two-Row Wampum) principles in partnership with Six Nations of the Grand River.
Bibliographic record
Abstract
As part of the Ohneganos research project, funded through the Global Water Futures (GWF), we document the ways we worked across Haudenosaunee and anthropological knowledge to assess the impact of water insecurity on holistic maternal health. This research was led by the Six Nation Birthing Center (SNBC), inspired by Haudenosaunee Kaswenta treaty principles. We utilized community-based participatory research (CBPR) and Indigenous research methods (IRMs), such as storytelling, to find common ground of dialogue and reciprocity. In doing so, this research goes beyond traditional anthropological ways of data collection and fieldwork and highlights the importance of active community direction and participation. We argue that different knowledge from the researchers does not need to be ignored or reduced to one singular perspective to work across worldviews. Instead, acknowledging and highlighting the differences will lead to innovative methods and scholarship. This paper contributes to the literature of research methods and policies and will be helpful to Indigenous communities and non-Indigenous researchers working together.
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 | 0.037 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".