Complex Constructivism: Rethinking the Power Dynamics of “Understanding”
Bibliographic record
Abstract
Constructivists herald their learning theories different frombetter than-direct instruction; however, deeply embedded in the mantra of constructivism is the idea that the teacher is the "understander" and the student is the "understandee."The act of interpretation is imposed on the "understandee" and there is no question that the "understander" -who determines that understanding has been achieved -is interpreting "correctly."This notion of understanding in educational research maintains the power dynamics of knower/learner.This power play is what I question and critique.Using complexity theory as a lens for this critique, I explore possibilities for dynamic and hermeneutic interpretations of what is being "understood," specifically through the recursive potential found in "complex" conversations.I draw on complexity research to elucidate how, in terms of "understanding," recursion is different from repetition.Learning and teaching do not need to be restricted to internal re-presentations of a world "out there."Experiences, interpretations, learning, teaching, and epistemologies are all dynamic negotiations that occur in-between.This space, which Ted Aoki (1996Aoki ( /2005) ) describes as "and/not-and," is "a generative space of possibilities, a space wherein tensioned ambiguity newness emerges" (p.318).Exchange of ideas occurs in this space, and these complex conversations allow for
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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 | 0.030 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.124 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".