Transforming Transformative Education Through Ontologies of Relationality
Bibliographic record
Abstract
It has been charged that transformative learning theory is stagnating; however, theoretical insights from relational ontologies offer significant possibilities for revitalizing the field. Quantum physics has led to a deep revision in our understanding of the universe moving away from the materialism and mechanism of classical physics. Some scientists observe that this shifting view of reality is catalyzing a profound cultural transformation. They have also noted significant intersections between the New Science and North American Indigenous philosophies as well as Eastern mysticism, all relational ontologies. These intersections as well as the theory of agential realism of Karen Barad, feminist physicist, are used to propose a next generation of transformative learning theory, one that is embedded in ontologies of relationality. The author came to relational ontology through environmental and sustainability education. This fruitful cross-fertilization helps illuminate a transformative approach to sustainability education or transformative sustainability education—which has not yet been explicitly theorized. Relationality demands an ethical, ontological, and epistemological transformation. The six criteria that emerge in the overlap between quantum physics, living systems theory from ecology, and Indigenous philosophies can reframe our understandings of transformative education, particularly toward socially just and regenerative cultures, completing the work of unfinished justice and climate movements. Pertinent to adult educators, Naomi Klein (2014) asks, “History knocked on your door, did you answer?” (p. 466).
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.050 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".