Response to Proulx: "Maturana is Not a Constructivist" … Nor is Piaget
Bibliographic record
Abstract
Readers of Complicity are most fortunate to have Jerome Proulx's paper distinguishing "Maturana and Varela's Theory of Cognition ... from Constructivist Theories."This paper sits as a fine companion piece to the Educational Theory paper by Brent Davis and Dennis Sumara (Fall, 2002), distinguishing various types of constructivism and situating complexity as an alternative to constructivism, one focusing neither exclusively nor heavily on the actions of the learner but rather on the interplay of factors or forces within a dynamic, learning situation.Proulx points out that "constructivist" (constructivism)a word Davis and Sumara note is not part of Jean Piaget's vocabulary 1 (p.411) -has become, in the hands of Ernst von Glasersfeld, a mantra for teachers dealing with children.1 While not a common part of his vocabulary, Piaget did, in the last year of his life (1980), use the words "constructivist" and "constructivism" (Piaget and Garcia, 1991, p. xii and p. 43 respectively).Davis and Sumara point out that Piaget's relation to the notion of "construction" is not immediately obvious.As a proclaimed structuralist, what Piaget asserted was, I believe, that the child constructs the structures or schemas necessary for his/her learning.How the child constructs these structures is an issue Piaget wrestled with all his life.
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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 | 0.011 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.022 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.074 | 0.102 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".