Lessons and consequences of the evolving 2007-? Credit Crunch
Bibliographic record
Abstract
We are neither economists nor academic scholars; however we are students of the markets\nhaving experienced the credit crunch on the front lines as institutional investors from a\ncountry that is neither in Europe nor is the United States (i.e. Canada). The credit crunch\nand related “Great Recession” have instilled havoc on the global economy. The crisis has\nled to a large contraction of the real economy of approximately 1% of real GDP in 2009,\nwhich could have been considerably larger without massive government sponsored stimulus\nplans. In the aftermath of every crisis there are always lessons to be learned. The main\ntakeaways from the most recent credit crunch centre on risk distortion, the flawed counterparty\nrisk offset model, excessive leverage, inherent conflicts of interest and the legacy\nof creating “too big to fail” financial institutions. As financial markets appear to have\nstepped back from the brink of destruction, we believe that there are three major consequences\nthat we are currently facing. First the global financial system will likely be irrevocably\nchanged by new regulations. Second, on the economic front, we are facing a\npost-recession period of relatively low global growth. Third, developing countries’ governments\nare facing massive budget deficits and their debt/GDP levels are likely unsustainable\nand therefore requiring severe fiscal austerity programs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".