Afrofuturism and Indigenous Futurism are Democracy: STEM Possibilities for Humanizing Pedagogy in Education
Bibliographic record
Abstract
How can STEM education benefit from a paradigm shift, beyond economic growth and citizenship development, towards alternative perspectives for problem-solving to combat anthropogenic dilemmas? This conceptual essay investigates why we should incorporate Afrofuturism and Indigenous Futurism into STEM education to expand possibilities for more humanizing pedagogy and praxis. I argue STEM innovation has been at the forefront of enacting democracy in the United States. First, I provide a critical examination of STEM, during the colonial era and Early Republic period, by reframing goals of U.S. democracy as being closely aligned with technological innovation. The entry point is late 20th century and early 21st century educational reform and then shifts to scrutinize land extraction tactics and the institution of schooling during the 18th and 19th centuries. Next, I concentrate on how 20th century societal STEM trends influence curriculum as well as normalized beliefs about STEM fields. Lastly, I advocate for Afrofuturism and Indigenous Futurism as nuanced extensions of art-integration into STEM. By centering Afrofuturism and Indigenous Futurism in 21st century STEM education and research, we can better actualize U.S. democratic ideals oriented toward valuing whole persons, especially concerning the Anthropocene. Keywords: Afrofuturism, Anthropocene, democracy, (re)humanizing pedagogy, Indigenous Futurism, and STEM innovation
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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 | 0.003 |
| 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 | 0.032 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".