Bibliographic record
Abstract
The previous chapters progressively introduced several of the core themes which underpin this problematic and the book: authoritative knowledge, risk/uncertainty and performativity. Acquainted with this knowledge, and equipped with the necessary analytical tools to problematize credit ratings, we are in a better position to understand how the problem of sovereign creditworthiness, and thus the ratings space, is constituted through its (calculative) assessment and articulation. Consequently, the misrepresentation of uncertainty as risk reinforces the depoliticizing effects of ratings as a ‘qualculative’, socio-technical device of control and governmentality; whereby informal (read political) judgment in fiscal governance is marginalized and censured in favor of normalizing mathematical/risk models. Rather than ontologically predetermined, however, it is through the discursive practice of rating risk — and all the speculative investment activities which it enables — that a neoliberal politics of limits materializes. Timothy Mitchell (1998: 92) reminds us that: The invention of the economy required a great work of imagination on the part of economists and econometricians, to find methods of representing every relationship constituting a nation’s economic life and giving each one a value. At the same time the invention also required a process of exclusion. To fix a self-contained sphere like the economy requires not only methods of counting everything within it, but also, and perhaps more importantly, some method of excluding what does not belong. No whole or totality can be represented without somehow fixing its exterior. To create the economy meant also to create the non-economy. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".