Constructivism: learning theories and approaches to research
Bibliographic record
Abstract
This chapter promotes alignment of worldview, theoretical frameworks and research approaches in relation to constructivism and its philosophical underpinnings. It presents an overview of constructivist theories of learning. The chapter then focuses on constructivist approaches to research: its traditions and methods. It provides examples from various disciplines including clinical medicine, healthcare profession education and nursing. The chapter explains how the philosophy of constructivism gives rise to certain theories of learning which we rely on in our daily practice. It explains the role of the researcher in constructivist research. The constructivist researcher would accept that the information being shared through the interview process is the result of an exchange between the researcher and the participant, rather than the conveying of ‘pure, unfiltered’ fact. Authenticity criteria assess the fairness of research within the constructivist domain.
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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 | 0.084 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.025 | 0.020 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".