Bibliographic record
Abstract
The growing political salience of economic nationalism after the 2008 financial crisis has strengthened arguments made in pre-crisis political economy (PE) scholarship about the enduring importance of this ideology and the need for more study of the economic significance of nationalism and national identities. Scholarship on this topic suffers, however, from some blindspots that inhibit understanding of the two most systemically important strands of this ideology in recent years: those associated with American populist conservatism and Chinese developmentalism. Both can be described as neomercantilist forms of economic nationalism, a form most commonly identified with the ideas of Friedrich List. But List’s ideas are much less useful for understanding them than the distinctive ideas of two other historical thinkers – Henry Carey and Sun Yat-sen – who have been quite neglected in PE scholarship on economic nationalism. These empirical blindspots are related to two deeper conceptual ones: insufficient recognition of the diverse origins and content of neomercantilist economic nationalism and, in the case of Sun’s neglect, the Western-centric nature of PE’s intellectual history. If these blindspots can be overcome, PE scholars will be better positioned to interpret these – and other – diverse varieties of contemporary economic nationalism.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".