Challenges and opportunities of centring the African voice in disability research
Bibliographic record
Abstract
In 2020, the African Network of Evidence to Action on Disability (also known as AFRINEAD) hosted its 10th conference in Cape Town. This paper synthesises inputs by the three authors as plenary addresses, particularly focusing on the challenges and opportunities of centring African voices in disability research. Our concern in this article is to engage with the question of exclusion as an issue not just in the everyday lives of people with disabilities but also in the world of ideas - the ideational space. We suggest that a reimagined disability study depends on the centring of African experiences, voices and knowledges. This is especially so as there are African concepts that are not rigorously pursued in research. African Renaissance thinking makes allowance not only for critically reflecting on the historical and contemporary constructs of disability but also for fashioning a higher civilisation in which people with disabilities can exist within society as worthy and valued human beings.
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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 | 0.323 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.033 | 0.084 |
| Scholarly communication | 0.034 | 0.059 |
| Open science | 0.004 | 0.056 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".