Economic Expertise in Postcapitalist Democratic Economic Planning
Bibliographic record
Abstract
Postcapitalist democratic economic planning (DEP) seeks to democratize economic decision-making. In response to the failures of central planning, DEP is vigilant lest the emergence of some new elite subvert its democratic and egalitarian aspirations. This article considers the possibility that economic expertise intended to support DEP may be means through which new elites are covertly encouraged. DEP advocates seek to safeguard DEP processes from elite control by proposing institutional oversight structures combined with enhanced subjective oversight capacities. In the case of economic expertise, we contend that these responses mitigate the possibility that economists’ analyses will have preferential implications but do not resolve this antidemocratic possibility. Economic expertise poses a problem of democratic accountability because its technical opacity impedes democratic oversight, thus enabling the covert design of economic analysis in ways that favour some groups over others. We conclude by arguing that reconsidering economic expertise in postcapitalism can attenuate this tension.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".