Bibliographic record
Abstract
This paper draws upon the scholarship of interdisciplinarity to argue that Economics, like all disciplines, should be open to a wide range of theories and methods, and the study of all relevant phenomena. A classification of the different methods and theory types used by scholars identifies key strengths and weaknesses of each. Different schools of heterodox [that is, non-neoclassical] economics, as well as neoclassical economics itself, emphasize different sets of theory and method. Each thus has a unique contribution to make to a holistic understanding of the economy. At present, different heterodox schools, like neoclassical economics itself, tend to act as if it were thought that their theory and method were superior. This paper urges a quite different attitude: different heterodox schools, as well as neoclassical economics, should be seen as complements rather than substitutes. That is, the insights of different schools of thought within Economics can and should be integrated just as disciplinary insights are integrated within interdisciplinary scholarship. The classification also identifies valuable theory types not presently embraced by any heterodox approach. Heterodoxy needs also to embrace the causal linkages between economic and diverse non-economic phenomena; the paper outlines a strategy for organizing the complex understandings that emerge from such a project. Some might recoil at the complexity of an academic enterprise that embraces such a wide range of phenomena, theory, and method; this paper shows how these diverse investigations can be organized in terms of the classifications presented such that all economists could readily appreciate the contributions of others. The paper also makes suggestions regarding the daily practice of heterodox economists, and draws lessons for heterodoxy from interdisciplinary research practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| 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".