FinTech and the Emerging Digital Money Market: What Can We Learn from the Global South?
Bibliographic record
Abstract
Digital financial technologies, as enablers of digital currencies and financial services, have sparked the emergence of a new market in the financial industry and become one of the tools used by countries in the Global South to transform their economies and solve grand societal challenges. For example, the remarkably high adoption of Mpesa (Mobile Money) in Kenya illustrates how digital money can improve the livelihoods of a significant majority in the Global South who have been excluded from access to financial services or are underserved. At the same time, introducing these financial digital technologies and currencies (e.g. Blockchain, cryptocurrency, Mobile Money, Central Bank Digital Currency) is disrupting the business environment in the Global South, especially the traditional financial systems. New players such as telecom operators, crypto companies, and other FinTechs have entered the banking sector and are reshaping this business landscape. Understanding these transformations, challenges, mechanisms, and their impacts has become a scholarly imperative. This Symposium proposal assembles scholars intending to advance this emerging literature by discussing recent and ongoing research. Institutional Pressures in the Formation of Institutional Field: Case of the FinTech Field in Ghana Author: Melissa Selley Akosua Baba; University of Southampton Author: Daniel Gozman; The University of Sydney Author: Keiichi Nakata; Henley Business School Reconfiguring Financial Systems for Inclusion: An Experiment with Central Bank Digital Currency Author: Johnson Delali Oware; University of Ottawa Author: Saouré Kouamé; Telfer School of Management, University of Ottawa When Social Worlds Merge Inclusion Happens: A Case of Embodied Financial Inclusion in Ghana Author: Jadwiga Supryn; University of Oxford Digital Complementary Currencies and Reciprocity Economies in Kenyan Informal Settlements Author: George Kuk; Manchester Metropolitan University Author: Stephanie Giamporcaro; KEDGE Business School The Brazilian Landscape for Community and Municipal Currencies Author: Eduardo Henrique Diniz; Fundação Getulio Vargas Author: Ester Barinaga; Lund University Author: Luiz Arthur Silva de Faria; -
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".