The Naturalized Knowledge System: A methodology for community development.
Bibliographic record
Abstract
The extensive experience of the Haudenosaunee (Iroquois) Confederacy in adapting to their natural environment, and the distillation of this knowledge and the traditions of their peoples, offers us a comprehensive model for community development with the potential to overcome "crisis management" and begin planning for the future. Based on respect, equity, and empowerment, the Naturalized Knowledge System methodology enables us to evaluate, plan and promote community development at all levels---the individual, group, nation and confederacy. An adaptive tool, it learns from past mistakes and successes, integrating community development, cultural preservation, and environmental restoration into one process. The fundaments elements of the Naturalized Knowledge System methodology are developed in this paper, demonstrated in application to the community development of the Maleku First Nation of Costa Rica, and compared to other community development practices such as safety audits for women in urban environments, and creating place-based cultural representation in American cities. (Abstract shortened by UMI.)
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".