Bibliographic record
Abstract
Global financial and data capitalism has constituted new forms of knowledge, novel inscriptions which make that knowledge tangible and new ways of visualizing sources of value and profit. This paper examines a cluster of new practices designed to make visible – and extract value from – those without formal credit scores in contemporary financial markets. Many ‘financial inclusion’ projects now attempt to score the ‘credit invisible’ by drawing on a range of alternative data – non-financial payment streams, academic records, behavioural signals gleaned from online or social media footprints and results generated via digitized psychometric testing – and by assessing that data in relation to models of risk assessment based on the analysis of big data. I argue in this paper that these experiments in alternative credit scoring constitute the unbanked as an important, and dubious, category of knowledge and intervention. I also argue that attempts to score the unbanked offer a revealing glimpse of many of the social and political limitations associated with projects of ‘inclusion’. Although often imagined as forms of pristine incorporation, inclusion projects often constitute troubling new kinds of social sorting and segmentation.
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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 | 0.045 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.080 |
| Scholarly communication | 0.024 | 0.043 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".