Liberal Economics’ Track Record on Inclusion, Sustainability and Resilience
Bibliographic record
Abstract
Abstract This chapter summarizes the important historical contribution of liberal economics in accelerating economic growth and reducing poverty, while examining its profound and persistent difficulty in responding effectively to societal demands for greater social inclusion, environmental sustainability and resilience to major shifts and shocks. It traces a growing body of evidence and criticism that modern economics may be constitutionally incapable of addressing these considerations adequately, particularly since its theoretical models generally treat them as afterthoughts, matters assumed to resolve naturally over time on the strength of a rising tide of national income generated by economic growth. It then assesses the state of reform in each of these three respects, concluding that efforts to date have been essentially aspirational, procedural or incremental and thus are destined to fall well short of what would be required to fulfil the corresponding goals humanity's political leaders have set in multilateral agreements, such as the Sustainable Development Goals, Paris climate and Kunming-Montreal biodiversity agreement targets, and objectives of the ILO Centenary Declaration for the Future of Work. Despite the widespread call for replacement of “neoliberalism” and the Washington Consensus, a viable theory of more fundamental and sufficient change has yet to emerge from within the economics community. This will require a deeper critique and reformulation of liberal economic doctrine, starting with a re-examination of its first principles.
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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 | 0.002 | 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 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".