The conflicting logic of markets and the management of production
Bibliographic record
Abstract
Introduction Since the late 1990s, as the typical American family saw its income stagnate, the availability of easy credit and cheap imports fueled a consumer and real estate market boom. Households were able to use debt to supplement earnings and thereby live beyond their means, under the illusion of prosperity. The housing bubble and low interest rates added to the illusion as households were encouraged to increase borrowing, using the rising equity in their homes as collateral to fund additional consumption and investment in housing. The conditions that produced this consumer boom were less comfortable for investors and financial intermediaries. Low interest rates and the large volume of Asian savings in the market not only depressed returns on traditional financial instruments. It also generated increasing pressure from investors for higher yields which could be achieved in essentially two ways: higher risk and higher leverage. Many opted for both, using the booming property markets as fuel. In 2007, however, the US sub-prime bubble burst, plunging the global money markets and economy into crisis and revealing serious imbalances in the system as a whole.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.031 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".