Money and sociality: Measuring the unmeasurable money as justice, time and usury
Bibliographic record
Abstract
Levinas confirms: a reflection about a money as a social and economical reality is not possible without a serious analysis of empirical data. On the other hand, this reflection always involves something else, so a money is never a merely economical category. In that sense, Levinas proposes an intriguing meditation about some ?dimensions? of a money in the western tradition. Contrary to the traditional moral condemnation of a money - which however remains unquestionable because of the fact that a man always carries a risk of becoming a merchandise - Levinas suggests that money never simply means a reification, but always implies some positive dimensions. Levinas suggests that a money is not something morally bad or simply neutral covering human relationships, but rather a condition of human community. Furthermore, he claims that a money is a fundament of the justice. A money makes possible a community, he explains, because it opens up the dimension of the future, and implies the existence of human beings who give themselves a credit; a credit understood as a time and a confidence. We shall try to address some problems implied by this thesis, particularly the problem of the relationship between time, money and credit. Finally, we are going to ask whether this cred?it - inseparable from the very essence of the money - is not always already a sort of usury.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".