Chains of Finance: How Investment Management is Shaped
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Investment is no longer a matter of individual savers directly choosing which shares or bonds to buy. Rather, most of their money flows through a 'chain': an often extended sequence of intermediaries. What goes on in that chain is of huge importance: The world's investment managers, who are now almost as well paid as top bankers, control assets equivalent in value to around a year of total global economic output. In Chains of Finance, five social scientists discuss the ways in which the intermediaries in the chain influence each other, channel the flows of savers' money, enhance investment decisions, and form audiences for each other's performances of financially competent selves. The central argument of the book is that investment management is fashioned profoundly by the opportunities and constraints this chain creates. Whether chains constrain or enable, however, they always entangle, tying intermediaries to each other - silently and profoundly shaping the investment management industry. Chains of Finance is a novel analysis that will make students, social scientists, financial professionals, and regulators looking at the workings of financial markets in a new light. A must-read for anyone looking for insights into the decision-making processes of investment managers and those influenced by and working for them.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it