Making Money, (Re)Making Firms: Microbusiness Financial Networks in Birmingham's Jewellery Quarter
Bibliographic record
Abstract
Although economic-geographical research has recently been the subject of ‘cultural’, ‘institutional’, and ‘relational’ turns that stress the situated, relational, and embedded nature of economic activity, these concerns are usually elaborated through analyses of industrial, not financial, circuits of capital. In this paper I address the neglect of the financial elements of production networks by exploring the geographies of some of the financial practices of a group of microbusinesses in Birmingham's Jewellery Quarter. I argue that the Jewellery Quarter is an important financial space for these firms and illustrate how their production regimes are produced and reproduced through different spatiotemporal financial relationships with suppliers, customers, and financial intermediaries. By contrast with the undersocialised treatments of agency that predominate in firm finance literatures in economics and finance, I illustrate the situated, idiosyncratic, and often very personal nature of the financial knowledges, practices, and networks that reproduce these firms. This financial ‘cut’ through the Jewellery Quarter treats firm finances as integral to firm behaviours and strategy and uses this vantage point to assess the potentials and predicaments facing these firms that are regarded as strategically important for the future of the Quarter.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".