Beyond austerity: pro-public strategies versus public-private partnership scandals
Bibliographic record
Abstract
Three decades of neoliberal era spending restraint have left countries around the world with a public infrastructure investment crisis. By 2030, according to one prominent estimate, the global need for infrastructure spending will total US$57 trillion (McKinsey Global Institute 2013). This infrastructure investment crisis comes at a time of renewed austerity with governments of all stripes committed to balancing budgets and paying down debt in the wake of the 2008 GFC. Recent austerity matches well-established neoliberal currents, coalescing to reconfigure sources of revenue for public works such as drawing on private finance to pay for state infrastructure through public-private partnerships (PPPs), and the further institutionalization of PPP through new generation initiatives like the Canada Infrastructure Bank. However, as explored here, the normalization of PPPs today ignores a long run, scandalous track record: market monopolization by corrupt and inept private partners, troublesome bankruptcies and bailouts, and national revenue extraction through private partner equity rights and offshoring practices. Thus the chapter argues that alternatives to austerity require not only more spending, but also new types and sources of spending – namely, finding alternatives to private financing for public infrastructure. Alternative strategies include enacting ‘pro-public’ reforms to public sectors and mobilizing national sources of pooled savings for community-oriented purposes. The effects of the 2020 pandemic pandemonium are equally significant though they appear at this stage to be following familiar lines with governments encouraging PPPs despite their drawbacks, and the need for enhanced commitments to pro-public strategies being all the more dire.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.034 | 0.030 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".