Explaining the Origins and Evolution of the Global Financial Inclusion Agenda
Bibliographic record
Abstract
The idea of “financial inclusion,” understood as the access to and use of a broad range of retail financial services (including bank accounts, payment services, credit, and insurance) by everyone in society, emerged as a global priority in the late 2000s. Financial inclusion now features prominently in global economic governance and the activities of disparate international organizations, states, businesses, and civil society organizations. This dissertation asks: what explains the origins and evolution of the global financial inclusion agenda? Existing scholarship often emphasizes the interests and power of Western states and businesses, asymmetric debt and power relations, and the centrality of class conflict. In contrast, I offer a more complete explanation by interrogating the agenda’s ambiguity and coalitional politics. Ambiguity is typically conceptualized as a tool deliberately used by an entrepreneur for the purpose of expanding support. I introduce the novel concept of participatory ambiguity, defined as the process by which entrepreneurs and coalition members construct multiple cognitive frames around a central idea. In so doing, I theorize how ambiguity is co-produced by disparate actors who use language (or branding) and creative action to legitimate multiple policies and outcomes and secure space for their own interests. I argue that the origins and evolution of the global financial inclusion agenda are best explained by participatory ambiguity and the specific mechanisms of quantification, institutional layering, and coordination effects. Empirically, I draw on more than 70 interviews and archival documents from three countries (Ghana, United Kingdom, United States), as well as quantitative text analysis on an original collection of 49 national strategies. Further, I combine variation in global support for the agenda over time with a “most likely” country case study (Ghana) and “least likely” issue area (humanitarian assistance). My research identifies how ambiguity mitigated coalitional conflict and enabled the incorporation of key constituencies, specifically those associated with global financial security or financial stability. This dissertation thus contributes to constructivist scholarship on the agency and power of global South actors in global politics, as well as research on the role of ideas and ambiguity in coalitional politics.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 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".