Growing out of Crises and Recessions: From Regulating Large Financial Institutions To Redefining Government Responsibilities
Bibliographic record
Abstract
I characterize and discuss in this paper the challenges and pitfalls we must face to grow out for good of the recent and future financial crises and economic recessions. I propose a brief history of the last crisis and insist on the loss of confidence within the banking and financial sector, which propagated later to the real sector. I discuss ways to rebuild confidence and move out of a stable bad economic equilibrium, due in part to inefficiently designed bonus systems. Considering data on gross job creation and loss in the private sector, I challenge the sorcerer’s apprentices plan for reforming capitalism and I recall the role of creative destruction. I show that government deficits and economic growth are not good friends and I offer a reference to the Canadian experience of the two decades 1985-2005. Finally, I discuss fiscal and regulatory reforms and propose redesigned roles for governmental and competitive sectors in generating a more prosperous economy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".