Finding fault lines in long chains of financial information
Bibliographic record
Abstract
IPE has usefully identified numerous contributors to financial crises. Considerably less attention however has been granted to the roles of financial infrastructures, considered in this special issue as the socio-technical systems enabling basic yet crucial financial functions to be carried out, but that tend to be taken for granted and assumed. This article argues that vulnerabilities in information flows enabled through connections between globally dispersed human actors and non-human objects have shaped the types of events triggering crises, how such periods of instability unfold, and their eventual resolution. Building on insights from actor-network theory, we illustrate how fault lines in ‘long chains’ of financial information conditioned three financial earthquakes between the 1980s and the present. Our analysis bridges insights from accounts that tend to separately emphasize material and ideational roots of crises. It also points to the importance of supplementing the stress on quantitative indicators with efforts to identify and address vulnerabilities in the quality of connections between disparate actors and objects that enable or disrupt flows of information facilitating global financial markets.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".