Bibliographic record
Abstract
Financial inclusion is a relatively new phenomenon.It was rooted in the Arab Spring movements and had been booming in Africa during the past decade.Today, more than sixty countries worldwide have a financial inclusion strategy, and still, 1.7 billion people around the world remain unbanked.Various influences impact the degree of financial inclusion in a country, i.e. education or political stability.Although financial inclusion is most commonly found in developing countries, scholars have found that a lack of education explains financial exclusion in developed countries.Education as a requirement for financial inclusion is the backbone of my argument.The paper tries to answer the question; does more educational spending lead to more financial inclusion?Although there is evidence from different countries that education remains an important issue in the whole financial inclusion debate, this paper demonstrates no direct relationship between governmental expenditures on education and financial inclusion.Still, the four-country analysis investigates various issues such as the role of government, the role of international institutions, the risks of digitization, and concludes that financial inclusion does not always mean financial inclusion.'Financial education should focus on the most pressing issues -in other words, those that do most harm if not addressed.Some issues are particularly urgent, such as helping consumers to stay safe in this digital age.' Angel Gurria, OECD Secretary-General
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".