Shadow Networks: Financial Disorder and the System that Caused Crisis
Bibliographic record
Abstract
The networks and institutions that support a finance-focused, market-centered model of economy and society from their intellectual roots through their ascendancy to their surprising resilience in the face of manifest failures are traced. The focus is on the quarter century, 1980–2006, leading to the global economic crisis and on the now decade-long crisis itself (2007–17). The approach uses political economy, with a focus on actors and their motives, the structures and resources that shaped them and that they in turn shaped, and the key events and turning points. The actors vary but come overwhelmingly from different branches of the power elite: investment bankers; finance ministers; bearers of dynastic wealth; college professors; government regulators; and central bankers. Their resources take many forms, from academic articles and white papers to cultural production, palace intrigue, and elections. A particular interest is taken in how the actors have mobilized institutions and networks to maintain the key tenets of the model despite the serious flaws indicated by the rise of inequality and the financial crises of both emerging and advanced economies at the dawn of the twenty-first century (Cit in Oxford, 2018)
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".