What do undergraduates study in heterodox economics programs? An examination of the curricula structure at 36 self‐identified programs
Bibliographic record
Abstract
Purpose This paper aims to review the undergraduate curricular structure of 36 self‐identified heterodox economic programs in the USA, Australia, UK and Canada. Design/methodology/approach The author gathers, summarizes, compares and contrasts the structure of 36 undergraduate heterodox departments. Departments are classified into traditional, plausibly pluralistic, and demonstrably heterodox programs. Specific examples illustrate each classification. Findings With notable exceptions described here, most heterodox economics programs are structured as traditional mainstream departments with a few pluralist or political economy electives available. However, 20 departments exist that require at least one heterodox course; eight require two or more. Practical implications A few programs have created imitable curricular structures that one would expect to significantly influence the depth and breadth of heterodox perspectives presented in the undergraduate economics major. Originality/value This is the first published analysis of undergraduate heterodox economics curricula. It highlights the creative structures characterizing some of the English‐speaking world's best programs and demonstrates that the curricula in most programs lack required courses in heterodox economics. The paper also provides examples of intentionally heterodox programs that may serve as models for others to emulate.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".