[Re]claiming Indigenous Knowledge: Challenges, Resistance, and Opportunities
Bibliographic record
Abstract
In May 1994, I arrived in Kenya to carry out my research on Africa and, more specifically, rural Kenyan women’s Indigenous ways of knowing. My interest in this research was sparked by the lack of textual knowledge of African Indigenous knowledges during my tenure in three North American universities. As a young scholar, I “ran” away from Kenya because, all through my education, there was a great emphasis on western education, lifestyle, and culture. I longed for it, hungered for it, and worked hard to acquire it. I was convinced that, once I enrolled in a University outside my country, the curriculum would in some way touch on African ways. In this regard, I was mistaken. The paper has four parts: 1) the introduction and a brief background to my interest in Indigenous knowledges; 2) the method employed; 3) a discussion of two knowledge claims. The two knowledge claims represent part of my research findings; 4) a pseudo-conclusion, given that this work involves my search for self, a process which has been ongoing since the nineties.
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 | 0.048 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.032 | 0.087 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.015 |
| 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".