Learning in a Credit Crisis, or a Crisis of Credit? Microcredit Lending, the Grameen Bank, and Informal Learning
Bibliographic record
Abstract
Popular economic discourses have developed a lexicon which will often refer to economic downturns as a credit crisis. Kojin Karatani’s recent reworking of Marx’s analysis of the credit system however forces us to consider that credit itself may be the problem – a problem referred to here as the crisis of credit. This essay is a critical theoretical analysis of the way in which microcredit lending programs constrain informal learning so that people learn in a crisis of credit. After tracing the roots of microcredit to neoliberal gender reforms, I identify and critique three underlying justifications of the crisis of credit. Finally, I argue that understanding the credit/indebtedness process reveals how our proposed solution to a credit crisis contains new constraints for informal learning. Les discours économiques populaires font généralement référence aux récessions économiques alors qu’ il s’agit de crises de crédit. La nouvelle perspective du système de crédit de Marx entrepris par Kojin Karatani nous force cependant à considérer que le problème se pose sur le crédit lui-même – un problème en l’occurrence de crise de crédit. Cet article est une analyse théorique qui critique la façon dont les programmes de microcrédits entravent l’apprentissage informel des citoyens dans une crise de crédit. Après avoir retracé l’origine des microcrédits jusqu’aux réformes néolibérales de genre, trois justifications de taille ont été identifiées et critiquées. Pour conclure, j’argumenterai que le fait que comprendre le processus de crédit et d’endettement dévoile comment la solution proposée à la crise de crédit contient de nouvelles contraintes pour l’apprentissage informel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".