Understanding technological change in global finance through infrastructures
Bibliographic record
Abstract
Amid escalating claims about the promises and perils of emergent financial technologies (fintech), critical investigation of the extent to which specific technological changes in global finance are truly ‘disruptive’ is sorely needed. Yet, IPE has engaged little with the growing focus on fintech in popular and regulatory debates, as well as in Social Studies of Finance (SSF). This article and accompanying special issue foreground ‘infrastructures’ as a heuristic for injecting nuance into debates on the emergence, limits and implications of technological changes in global finance while bringing IPE into conversation with perspectives on fintech in cognate literatures. Building on insights developed in Science and Technology Studies (STS), we argue that tracing the ways in which infrastructures enabling financial markets to operate are assembled out of multiple old and new socio-technical devices offers productive avenues for addressing key questions arising from several entanglements underpinning technological change. The findings of contributions to this special issue are linked to two key themes in debates on the impacts of technological change: financial inclusion and financial stability. Further avenues are proposed for examining the infrastructures in which technological change occurs in global finance and beyond, while fostering on-going dialogues between IPE, STS and SSF.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".