A Minimal Computing Approach to Southern African Language Resources
Bibliographic record
Abstract
This new collaboration between a historian of Southern Africa (RC) and a specialist in computational methods (WJT), is designed to draw on our respective backgrounds and provide opportunities to enlist students and other collaborators in research and teaching. Our goal is to create tools that can be used to help explain unfamiliar languaging in historical contexts. We follow the tenets of minimal computing (Risam & Gil 2022) and take the perspective of language as a complex adaptive system (Kretzschmar 2015). We also situate our work within the postcolonial digital humanities generally (Risam 2018) and the specific critique of knowledge production and racism that Fields & Fields (2012, pp. 5-6) identified as ‘racecraft’, which “highlights the ability of pre- or non-scientific modes of thought to hijack the minds of the scientifically literate”. As practitioners of academic language research and computing, we must be attentive to the history of colonizers trying to not only kill ‘native languages’ but their speakers and cultures (Ngũgĩ wa Thiong'o 2009). To date, we have partially implemented one prototype for automating interlinear morphemic glossing of chiShona and English as shown in Figure 1 (Charumbira et al 2023). Here our intent is speculative design: to imagine a more inclusive space of computational tools and practices that jettisons some of the assumptions that have shaped the digital cultural record in the Global North.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".