Indigenous Standpoint Theory: ethical principles and practices for studying Sukuma people in Tanzania
Bibliographic record
Abstract
Abstract Indigenous Standpoint Theory (IST) is yet to be widely applied in guiding the conduct of research that involves Indigenous people in Africa. In reference to Tanzania, this approach is new. There has been no study in the context of Tanzania which has used IST, despite the presence of many Indigenous people in the country. IST is widely used in Australia, New Zealand and Canada to guide the conduct of research when studying Indigenous people. In this paper, I show how I developed nine ethical protocols for conducting culturally, respectful and safe research with the Sukuma people in Tanzania and how I used those protocols within a research project on girls and secondary education in rural Tanzania. By developing these protocols, a significant new contribution to the area of IST in Tanzania and Africa in general has been established. These protocols may serve as a starting reference point for other future researchers in Tanzania if they apply IST in their research such that the voices of Indigenous people may be heard, and the community has a greater degree of control and input in the planning and designing of the project, as well as the analysis and dissemination of the information.
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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 | 0.081 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".