Problem-based learning and mainstream economics: post-Keynesian economics to the rescue?
Bibliographic record
Abstract
Problem-based learning has real-life problems as a point of departure. Subsequently, theory and methodology need to be tailor-made to the problem under investigation. Students making problem-based economically oriented projects need to construct their own theoretical framework, often by combining different economic paradigms, linking to other social sciences and consulting previous, often interdisciplinary, research concerned with the problem at hand. These requirements constitute a challenge for mainstream economics, where theories are built on assumptions that are often very remote from real-life economics. Real-world economics, like Post-Keynesian economics, is far better suited to problem-based work, because they employ open-system analysis, which can be supplemented from other schools of thought and social sciences. Often problem-based learning-students deal with problems which require them to relate to previous studies of the problem at hand as well as mainstream and heterodox economic theory. This complexity makes students dependent on the advice and guidance of their supervisor, which needs to be open-minded, pluralistic and familiar with different economic paradigms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".