The rise of neoliberalism: how bad economics imperils health and what to do about it
Bibliographic record
Abstract
The 2008 global financial crisis, precipitated by high-risk, under-regulated financial practices, is often seen as a singular event. The crisis, its recessionary consequences, bank bailouts and the adoption of 'austerity' measures can be seen as a continuation of a 40-year uncontrolled experiment in neoliberal economics. Although public spending and recapitalisation of failing banks helped prevent a 1930s-style Great Depression, the deep austerity measures that followed have stifled a meaningful recovery for the majority of populations. In the short term, these austerity measures, especially cuts to health and social protection systems, pose major health risks in those countries under its sway. Meanwhile structural changes to the global labour market, increasing under-employment in high-income countries and economic insecurity elsewhere, are likely to widen health inequities in the longer term. We call for four policy reforms to reverse rising inequalities and their harms to public health. First is re-regulating global finance. Second is rejecting austerity as an empirically and ethically unjustified policy, especially given now clear evidence of its deleterious health consequences. Third, there is a need to restore progressive taxation at national and global scales. Fourth is a fundamental shift away from the fossil fuel economy and policies that promote economic growth in ways that imperil environmental sustainability. This involves redistributing work and promoting fairer pay. We do not suggest these reforms will be politically feasible or even achievable in the short term. They nonetheless constitute an evidence-based agenda for strong, public health advocacy and practice.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.070 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".