Seeing and Not-seeing Like a Political Economist: The Historicity of Contemporary Political Economy and its Blind Spots
Bibliographic record
Abstract
Contemporary political economy is predicated on widely shared ideas and assumptions, some explicit but many implicit, about the past. Our aim in this Special Issue is to draw attention to, and to assess critically, these historical assumptions. In doing so, we hope to contribute to a political economy that is more attentive to the analytic assumptions on which it is premised, more aware of the potential oversights, biases, and omissions they contain, and more reflexive about the potential costs of these blind spots. This is an Introduction to one of two Special Issues that are being published simultaneously by New Political Economy and Review of International Political Economy reflecting on blind spots in international political economy. Together, these Special Issues seek to identify the key blind spots in the field and to make sense of how many scholars missed or misconstrued important dynamics that define contemporary capitalism and the other systems and sources of social inequality that characterise our present. This particular Special Issue pursues this goal by looking backwards, to the history of political economy and at the ways in which we have come to tell that history, in order to understand how we got to the present moment.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.011 |
| 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".