Bibliographic record
Abstract
This paper provides a model and case study for how we theorize the phenomenon of hard times. In recent years the idea of ‘cynical reason’ has grown in popularity through the work of Slavoj Žižek and Fredric Jameson, for example. It holds that people are no longer shocked or surprised by government scandals, the blatantly self-interested actions of corporation, or politicians on the take. We are no longer naive; we know exactly how things work. And yet almost everyone seems content to continue on supporting our existing systems of democracy and economy. This thesis overturns older versions of ideology where ‘people know not what they do,’ and replaces it with the insight that ‘people know exactly what they are doing, and yet, they do it anyway.’ Does this repudiation of ideological analysis as a misrecognition of one’s actual conditions hold up when it comes to the great recession of 2008? Did people really expect a market collapse and understand why it occurred? This paper argues that hard times are usefully theorized according to the dual perspective of ‘system’ and ‘event.’ I show that our dominant schools of political economy (classical, neo-classical, Keynesian and Marxian) locate their explanations of the recession either in the economic system or in an appeal to a unique economic event, but not both. By contrast, I argue that system (conceived of as our overarching conditions of restraint and possibility) and event (conceived of as a convulsion that makes new types of collective action possible) can only be worked through together and that a dialectical approach is required in order to do so. A superior explanation of the recession, and possibly hard times generally, is provided by thinking of it as a 'systemic event.' I conclude by affirming the power of ideology today to obscure the system-event relationship, and the necessity of ideological analysis to articulate it.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".