Drawing on Indigenous Ways of Knowing: Reflections from a Community Evaluator
Bibliographic record
Abstract
Abstract: The clash between Western and Indigenous ways of knowing has been epitomized by the “parachuting model” of the Western researcher who drops onto the reservation, collects data, and leaves, never to be heard from again. The strengths of indigenous science, for example, observation and contextual factors, are either ignored or appropriated. These past (and sometimes present) wrongs committed by academic researchers continue to be a contentious issue in Native communities, where, despite the research dollars flowing into the community to “solve” health problems, disparities between Native health status and that of the general population persist. This article shares reflections from a community-based evaluator who, along with a Lakota health educator, served as “cultural translators” in a community participatory process led by a community agency. We recognized the need to work with/in two cultures—both the academic research world and the Native community—and drew on collaborative evaluation principles and indigenous ways of knowing to conduct formative evaluation research on smoking cessation issues for pregnant Native women.
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 | 0.090 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.047 | 0.044 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".