Bibliographic record
Abstract
In his "The Good, the Bad, and the Ugly: The Many Faces of Constructivism," Denis Phillips tries to provide a clear understanding of the range of various positions involving constructivism.In the first section of this essay, we present Phillips's way of viewing the various forms of constructivism and his framework for comparing the constructivisms.According to Phillips, there is a (very broad and loose) sense in which all of us are constructivists. 1In the second section, we argue that Phillips has not presented all of the prominent "constructivist" possibilities, for his framework has not provided for pragmatic realism.Although the reinvention of pragmatism is associated with Richard Rorty and Hilary Putnam, 2 Catherine Elgin and Israel Scheffler have also developed a plausible pragmatic realism. 3We explicate some of the key features of pragmatic realism and then compare it with Phillips's viewpoint.In developing our argument, we use a framework that takes into account the interrelations among epistemology, ontology, and (theories of) truth.We draw out some of the educational implications of a plausible pragmatic realism in the last section.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".