African Indigenous Knowledge Systems: Experts at the Intersect of Environmental Sustainability and Legal Precedent
Bibliographic record
Abstract
Climate change and the direct threat thereof weighs a dual burden; scientifically, on thedisciplines at the front line of the issue and politically, on the global governance systemwith matters of sovereignty, regulation, and compliance. The former is typicallycategorized as the science, technology, engineering, and mathematics (STEM)disciplines, while the latter is principally recognized as the following instructions: theUnited Nations (UN), the African Union (AU), the European Union (EU), and the WorldTrade Organisation (WTO). This research paper challenges to characterization of expertsand in turn exposes the systemic lack of consultation with key leaders for more policyinformed,sustainable decision-making regarding climate change. Who is considered anexpert? What do they look like? What credentials do they hold? Where do they comefrom? These are the critical questions concerning today’s gap in the approach to climatechange gathered from empirical data on the scientific community and from internationaldebate on the topic. This paper critiques the paradoxical nature of Western Eurocentricscientific knowledge systems as they impose standardized “solutions” across the worldwithout the consideration of the rest of the world. The global consensus amongst theactively publishing community of scientists is that 97% of the climate crisis is humancaused(National Aeronautics and Space Administration, 2023). Typically, these scientistsare comprised of a majority white male demographic who wear white lab coats and areoften distanced from the on-ground situation. Addressing such systemic limitations andexclusionary practices creates the research question of “how have humans interacted withthe environment prior to and independent from Western Eurocentric scientific knowledgesystems?”
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".