Tanzania’s National Financial Inclusion Framework and How It Facilitated Financial Inclusion: A Forgotten Political-Economic Story
Bibliographic record
Abstract
This article brings the National Financial Inclusion Framework (NFIF) into the political-economic literature and discussions lacking in political-economic debates. Financial inclusion has become a global political-economic agenda that tries to reverse the higher levels of financial exclusion globally. However, political-economic and development literature has done little to discuss the issues of financial inclusion and exclusion as we currently perceive them through a neoliberal lens and innovative financial activities. These activities may include inclusive digital financial services such as mobile money. This article tries to answer the following research question: ‘how does the NFIF support financial inclusion in Tanzania?’ This article explores the NFIF, its successes and issues in Tanzania. The content analysis of relevant official documents and the literature, on the one hand, and the in-depth unstructured interviews, on the other, have been used as the primary data collection methods. The findings indicate that the NFIF has been instrumental in facilitating the success of financial inclusion in Tanzania, despite its issues. From Tanzania’s study, the conclusion is that given a conducive and supporting environment, the NFIF has facilitated the success of financial inclusion in Tanzania.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".