Bibliographic record
Abstract
Recent financial turmoil has put emphasis once again on the very meaning and reach of ‘finance’. In doing so, recent financial crises have also provoked questions about the very ‘ends’ of finance: Where are the borders of finance? Given the expansive reach of financial innovation over the past two decades, are there any serious limits to the kinds of practices that can be converted into financial objects? Does the culture of finance (expansive and all encompassing) encounter meaningful interruptions? This paper explores these questions by reviewing a cluster of public-art responses to the 2008 financial crisis mounted by artists critical of the expansive logic of financial abstraction. This paper pays particular attention to the work of Fergal McCarthy and Fred Forest, two public artists who have confronted finance and its rational culture with practices of gameplay, whimsy, and carnival. In doing so, these artists invoke a strategy designed to lay the all-encompassing claims of financial abstraction alongside its own impossibility; alongside performances which undermine the expansive claims of financial abstraction. These are strategies, I conclude, which can interrupt the technocratic discourses which dominate the contemporary cultures of finance; strategies which, in the words of one artist, evoke ‘plausible states of uncertainty’ about our faith in financial abstraction.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".