Two-Eyed Seeing and the Synoptic Transfer Framework: Braiding holistic and scientific ways of living in Education for Sustainable Development
Bibliographic record
Abstract
This paper explores the integration of holistic knowledge systems with Western scientific perspectives through the concept of Two-Eyed Seeing (TES). By examining philosophical underpinnings of TES, particularly through the lens of Wilfrid Sellars' synoptic view, the paper highlights the potential for creating a more comprehensive understanding through the concept of Two-Eyed Seeing with epistemic insight. The Synoptic Transfer Framework (STF) as a Western educational variant of TES inspired by Sellars’ philosophy is introduced. TES, as conceptualized by Indigenous Elders, and the Western STF both advocate for a multi-perspective approach to the world, where the scientific and holistic images complement rather than compete with each other. The paper also addresses the chances and challenges of applying TES in educational contexts, particularly in Education for Sustainable Development (ESD), identifying common pitfalls such as the naturalistic and the moralistic fallacy. Furthermore, it argues for the importance of educational frameworks that incorporate TES to foster culturally responsive and inclusive science curricula. The study underscores the relevance of TES and STF in addressing sustainability issues, emphasizing the sentient persons approach and the normative perspective in both. The braid metaphor is used to illustrate the continuous, dynamic interaction between perspectives rooted in factual evidence and societal values to ensure a sustainable future for humanity. The Synoptic Transfer Framework ('Two-Eyed Seeing'). Explanation in the text. Adapted from Zeyer (2024) .
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".