African Indigenous Knowledge Systems: Experts at the Intersect of Environmental Sustainability and Legal Precedent
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it