Fin-Tech As a Destructive Force in the Field of Local Economic Development
Bibliographic record
Abstract
The so-called ‘fin-tech revolution’ represents one of the most important recent developments in the banking and financial services sectors, possibly, some say, as important as the industrial revolution itself. This is especially the case with regard to the Global South, where the fin-tech industry is said to possess the potential to make an historic positive contribution to sustainable local economic development and poverty reduction. However, the emerging evidence from Africa, where the fin-tech industry has developed most rapidly in recent years, and in particular from Kenya – seen as the ‘best practice’ country example of the fin-tech model in action – strongly suggests that this optimism is wildly misplaced if not entirely fraudulent. On the basis of current trends and developments, in fact, a stronger argument can be made that the fin-tech industry as it is currently structured will very seriously disadvantage and even further marginalise and disempower the global poor.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".