“Into Human Flesh and the Human Heart”: On Promotionalism and the Long Con of Fintech Credit-Scoring
Bibliographic record
Abstract
Fintech start-ups, such as Zest AI and LenddoEFL, promise enhanced levels of financial inclusion via the creation of “re-socialized” credit profiles derived from accessing clients’ online banking habits and social media accounts. As our social data becomes credit data, the performance of “appropriate” online selfhood can now, quite literally, become money. This article explores the reputational demands, disciplines, and contradictions of ostensibly alternative computational/platformed credit scoring. It argues that the world of “surveillance capitalism” involves the maintenance of a relentlessly promotional value chain. As we are summoned to assiduously self-promote online in pursuit of a creditable reputation and financial inclusion, the self-reflexive promotional logics of the platforms themselves work to remake the world in their own image, paradoxically undermining the productive economic assumptions upon which they are predicated.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.095 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".