Growing Out Of The Crisis Through Prudential Regulation Of Large Financial Institutions And Redefined Government Responsibilities
Bibliographic record
Abstract
I consider in this paper the challenges and pitfalls we must face to grow out for good of the recent and latent financial crisis and economic recession. I consider a brief history of the 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 in 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 reforms and renewed roles for governmental and competitive sectors in generating a more prosperous economy as well as some specific challenges we are facing today, in particular to redesign the regulatory framework of the financial sector Je considère ici certains défis et écueils auxquels nous devons faire face pour sortir pour de bon des récentes et rampantes crise financière et récession économique. Je considère un bref historique de la crise et insiste sur la perte de confiance dans le secteur bancaire et financier, qui s'est propagé plus tard au secteur réel. Je discute des moyens de rétablir la confiance et de se sortir d'un équilibre économique mauvais mais stable, dû en partie à des systèmes incitatifs mal conçus. Considérant les données sur la création et la perte brutes d'emplois dans le secteur privé, je nous mets en garde contre les apprentis-sorciers en mal de réformer le capitalisme et je rappelle le rôle important et trop souvent oublié de la destruction créatrice. Je montre que les déficits publics et la croissance économique ne sont pas de bons comparses donnant en référence l'expérience canadienne des deux décennies 1985-2005. Enfin, je discute des réformes fiscales et des rôles renouvelés des secteurs gouvernemental et concurrentiel dans la génération d'une économie plus prospère, ainsi que certains défis spécifiques auxquels nous sommes confrontés aujourd'hui.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".