Bibliographic record
Abstract
Taking up the geographer's task of following and defetishizing the commodity, this research taps into the United States (US) federal banking data to locate the commodity “money”. Law is used to specify money's locations. Relative to the size of its economy, Utah's banks report a lopsided share of US money. This paper unmasks important social relations embedded in the money commodities located in Utah's banks by tracing the history of US banking law, which has played a leading role in the processes responsible for Utah's outsized share of the sub-national monetary landscape. Banking law determined the scope and type of business in which banking firms and their corporate affiliates could engage. Throughout the 20th century, investment banks and commercial firms struggled to claim legal rights to engage in business combinations once deemed illegal: combining non-banking business with a commercial bank. The state of Utah, in coordination with financial and commercial firms, has expanded the legal and financial space of Industrial Loan Banks (ILBs), historically idiosyncratic chartered banks exempt from regulations separating banking firms from non-banking business. Utah marketed their banking charters to global, systemically important financial institutions and large commercial conglomerates, which then established or acquired ILB subsidiaries within the state. From Utah, the die had been cast: the largest non-banking firms on the planet were now legally empowered to accumulate capital in ways that had heretofore been forbidden at other locations. American banking had been transformed.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".