Credit rating agencies and the sovereign debt crisis: performing the politics of creditworthiness through risk and uncertainty
Bibliographic record
Abstract
As member states struggle to retain the investment grades necessary to allow them to finance their governmental operations at a reasonable cost, credit rating agencies (CRAs) have been blamed for exacerbating a procyclical bias which only makes this task more difficult. How CRAs contribute to the constitution of the politics of limits underpinning the European sovereign debt crisis is at the core of this article. As a socio-technical device of control, sovereign ratings are an ‘illocutionary' statement about budgetary health, which promotes an artificial fiscal normality. Subsequently, these austere politics of creditworthiness have ‘perlocutionary' effects, which seek to censure political discretion through normalizing risk techniques aligned with the self-systemic, and thereby self-regulating, logic of Anglo-American versions of capitalism. The ensuing antagonistic relationship between the programmatic/expertise and operational/politics dimensions of fiscal governance leaves Europe vulnerable to crisis and the renegotiation of how the ‘political' is established in the economy. New regulatory technical standards (RTS) can exacerbated the performative effects on CRAs, investors and member states.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".