Bibliographic record
Abstract
This paper is based on the conviction that a socioanalytical perspective on capitalist greed—in general and in the context of the recent financial crisis in particular—requires a systemic perspective and must take into account the unconscious dynamics beneath the surface of contemporary capitalism. An excursus into the history and philosophy of greed demonstrates that it is a ubiquitous phenomenon, whose meaning refers both to the individual and ‘the social’. Using the psychoanalytic understanding of greed offered by the British psychoanalyst Melanie Klein as a starting point, I see greed as a psychotic dynamic that inhibits thinking, and limits reality to what is bearable and desired. Greed is neither a phenomenon that appeared with the onset of capitalism nor the decisive cause of the recent financial crisis, but it is inherent in the former and became most apparent in the latter. Subsequently, I will elaborate how competition is often fueled by excessive greed that intends to damage or even annihilate competitors, and is the source of corruption and/or fraud. The mere pursuit of maximizing profit, fostered and legitimized by economics for almost half a century, has had a major impact on the prevalence of greed in contemporary economy and the financial service industry in particular. In conclusion, I will refer to what the psychoanalyst Wilfred R. Bion calls ‘negative capability’ and offer some thoughts about how the psychotic dynamic inherent in greed could be individually and socially more balanced with non-psychotic thinking, which is capable of taking the broader ‘reality’ into account.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.078 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".