Indigenous Ways of Doing: Synthesizing the Literature on Ethno-Engineering
Bibliographic record
Abstract
This paper synthesizes the literature on indigenous ways of doing, what we call ethno-engineering. Indigenous societies have faced countless years of oppression at the hands of Western colonization and assimilation. Western literature on indigenous knowledge is expansive, yet a deliberate focal point on ethno-engineering in indigenous literature is missing. In this paper, we have collected literature on indigenous knowledge and synthesized articles specifically on ethno-engineering, setting the papers in contrast to Western-engineering praxis. Our literature review methods proceeded in two phases. During the first phase we accumulated relevant sources (N=87), compiled these in a database, and coded these with a 10-item coding framework. In the second phase, we sampled literature from the initial database (N=31) and coded these items more extensively using an inductively developed coding scheme. Our intent was to contribute to a starting conversation on indigenous engineering bringing it to forefront of social justice/engineering discourse.
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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 | 0.049 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.036 | 0.025 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".