Giving Credit Where Credit’s Due: Making Visible the Ex Nihilo Dimensions of Money’s
Bibliographic record
Abstract
Agency Money is one of the most important pieces of “social technology ” ever developed, but as an object of study in its own right it is neglected by the dominant or mainstream traditions not only in modem economics but also in sociology. (Ingham 1996: 508) The process by which banks create money is so simple that the mind is repelled. Where something so important is involved, a deeper mystery seems only decent. (Galbraith 1975: 18-9) In this paper, I suggest that a generalized confusion about where money comes from today is contributing to a failure to grasp the macro- and micro-economic implica-tions—as well as the socio-cultural implications—of the ways changes to monetary mechanisms, particularly those put in place since the 1970s, have contributed to, and are contributing to, social, political and ideological upheaval in our increasingly integrated global economy. Changes in the way money is created have contributed to the world we’re living in today; for example, an exponential rise in individual and national debt levels in countries such as Canada and the United States (Montgom-erie 2007: 160), the ongoing disappearance of the middle class (Dallinger 2013; Foster and Wolfson 2010), and a perpetually expanding financial chasm between the richest 0.1 per cent of the population and everybody else (Grant, The Globe and
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".