Two-Eyed Seeing and Scientific Holism in a New Science|Environment|Health Pedagogy
Bibliographic record
Abstract
Science|Environment|Health (S|E|H) is a new science pedagogy that aims at promoting the mutual benefit between the three educational fields of science education, environmental education, and health education. Holism and its conceptualisation has become an important topic in recent S|E|H work. In this paper, featuring the invited symposium of the ESERA special interest group 4 at the ESERA conference 2021, we suggest the concept of Two-Eyed Seeing as a basis for the definition of scientific holism in S|E|H. Two-Eyed Seeing as a metaphor was introduced by science education researchers working with Canadian aborigines. Based on Sellars’s concept of stereoscopic view, we conceptualize Two-Eyed Seeing in S|E|H through an ontological framework. We define scientific holism as the “eye switch” from the scientific image to students’ life-world image, a transition that we consider as equally important as the - more common - reductionist “eye switch” from students’ life-worlds back to the scientific image. Two-Eyed Seeing may then be understood as a continuous circulation of repeated “eye switches” between life-world image and scientific image. We illustrate this approach by three symposium contributions - communicating the meta-organism in school, scientific holism against eco- and health depression, and a holistic visual tool to approach S|E|H competences – and we discuss consequences for teaching and research in science education. We point out that, in a new S|E|H pedagogy and beyond, Two-Eyed Seeing may be a helpful extension to the well-established socio-scientific issues approach.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.057 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".