Bibliographic record
Abstract
Ubuntu storytelling is about engaging our relational selves. This is why my people the Ngoni say, “The story of one cannot be told without unfolding the story of many.” This means that the diverse and sometimes contradictory analysis of the same story is welcomed as long as it is exercised responsibly. If we relate to each other through storytelling then our Ubuntu storytelling is a research method. In this paper I share why and how using Ubuntu stories as methodology is an effective way to encourage Indigenous Ubuntu scholars to think about the endemic tools that make their scholarship accessible to our larger Black communities. The Ubuntu have always used the art of oral storytelling to extol the power of experience as a teaching tool because a story can allow a culture to regenerate itself. As a Maseko Ngoni, I highlight how we use Ubuntu storytelling to produce knowledge, by addressing the following themes: What Ubuntu storytelling is; why I use Ubuntu storytelling and how I address the challenges of using Ubuntu storytelling in a colonial context. I end with an example of an Ubuntu story.
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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 | 0.046 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".