Paved with good intentions: Misdirected idealism in the lead-up to 2008’s GFC
Bibliographic record
Abstract
Abstract Irresponsible lending practices on the part of financial institutions and proliferation of tradable derivatives were key causal agents of the 2008 financial crisis. However, it is less clear why, historically, loose credit arrangements were so widespread. Somewhat misleadingly, much conjecture has laid blame at the feet of financial institutions themselves. While it is true that duplicitous and even corrupt lending practices were consequential antecedents of the crisis, a legacy commitment to certain – laudable – elements of New-Dealism created context for these elements to become established. To understand really what went wrong, it is necessary to look back before the 2000s and appreciate the interaction that was occurring between a long-term policy commitment to neoliberalism and piecemeal/fragmented application of approaches that aimed to assist financially disadvantaged people. Using the analogy of heart-attack pathology to guide some of its analysis, this essay argues for better policy-integration.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| 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".