Inclusión financiera en Bolivia: Un estudio de caso para los productores quinueros del Altiplano Sur
Bibliographic record
Abstract
This paper analyzes financial inclusion in Bolivia through aggregate indicators of access, and develops a case study for quinoa producers in the Southern Altiplano. Using primary data from a survey conducted by INESAD, it evaluates the factors that affect the probability of quinoa producers having a savings account in a financial institution. In other words, financial inclusion is addressed within a demand perspective, understood as access to savings services. Considering three categories of variables -socioeconomic, educational and connectivity-, the results reveal that the holding of a formal account among quinoa producers largely depends on their level of education and their access to information and communication technologies through a mobile phone or computer. Financial education shows a positive incidence, but significantly more important for producers who are part of the Fair Trade production scheme. In terms of employment status, individuals who generate income are more likely to be included in the financial system. On the other hand, gender is not a significant factor on its owm; however, when interacting with school dropout, it is found that women with incomplete schooling are a vulnerable group to financial exclusion. Finally, proximity to branches or agencies is not a significant factor; however, road structure is: greater distance to a highway has a negative impact on the financial inclusion of quinoa producers in the Southern Altiplano of Bolivia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".