Varieties of ignorance in neoliberal policy: or the possibilities and perils of wishful economic thinking
Bibliographic record
Abstract
We might be tempted to view the recent efforts by political leaders to cultivate certain convenient forms of economic ignorance as characteristic of a novel ‘post-truth’ age. This article suggests instead that we take this troubling trend as an invitation to examine the role of ignorance more generally in political economic thinking and practice. Whereas many scholars have treated uncertainty and other unknowns as external challenges that can be resolved through expertise and learning, this article instead endogenizes ignorance, treating it as a key tool in the development of policy knowledge. Drawing on archival material from the early Reagan and Thatcher years, this article examines a moment when many of the contemporary assumptions about economic ignorance and strategies for coping with it were first developed and tried out. I introduce a typology of the practical role of ignorance in economic policymaking, ranging from wishful thinking, to confusion, fudging, denial and puzzling. Mapping the space between learning and lying, this article contends that ignorance plays two very different roles in the trajectory of many economic policies, allowing policymakers to discount the political effects of their economic actions while also opening up the possibility of genuine reflexivity about the limits of expertise.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.083 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| 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".