‘All data is credit data’: Constituting the unbanked
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it