Reconciling the Dual Worldviews of Ancient Wisdom and Modernity: Collaborative-Learning Implications for Future Discourse
Bibliographic record
Abstract
Science, climate change and traditional (or local) knowledge have been at the forefront of many academic and non-academic debates attempting to find discernible or explainable commonalities that exist between opposing worldviews (traditional knowledge/indigenous science vs. Western or Eurocentric Science). Ancient wisdom and modernity have seen their share of controversies over the past decade or more and, in particular, attended by many authors and scientists to explore these two important perspectives. This paper attempts to situate traditional knowledge and modern science by exploring the duality of ancient wisdom and modernity, and, in doing so, creates a better understanding of the importance of these opposing worldviews and how science ancient wisdom and technology/modernism can be interpreted and understood. The paper further explores meaningful interdisciplinary perspectives on how to explain coincidental relationships, components of bridging traditional knowledge/local knowledge (TK/LK) and transforming the compartmentalized view of science within a more holistic understanding of traditional ways of knowing. Lastly, merging Western or Eurocentric Sciences with Traditional Science has important policy implications that justify social-legitimacy through collaborative learning (CL) and integrating system thinking and conflict management.
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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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.098 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".