Bibliographic record
Abstract
Self-driving cars as a mode of transportation in Africa is probably more of a ‘when’ than an ‘if’ question. These autonomous vehicles that could benefit human safety by reducing human deaths from car crashes, may disadvantage Africans if the production of the technology does not adequately reflect African perspectives, including in the ethical constructions about rulemaking. To reduce or prevent technological racism, inequality, and the marginalization of Africans, actors in this field of autonomous transportation should investigate and challenge the differentials of power that will arise from the innovation. Ethical rulemaking for autonomous vehicles must be open, continuous, inclusive, collaborative and communitarian. Any ethical standards proffered should be cognisant of societal and environmental wellbeing and should ensure local sustainability and knowledge mobilization within the adopting states. Rulemaking on self-driving cars must go beyond ethics and should rely on international human rights norms and standards as a minimum core. Within the various instruments of international human rights law, the African regional system provides a unique perspective on human rights, and the African human rights instruments require new technologies to be cognizant of cultural norms and traditional knowledge, and to not perpetuate colonial constructions. An African perspective in this discourse on self-driving cars is relevant for its regional adoption and absorption, and it could also provide a more holistic, responsible, equitable, and accountable appreciation of the technology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".