Toward the Attainment of Sustainable Development Goals: The Impact of Transformative Service Research on Financial Inclusion
Bibliographic record
Abstract
Adopting approaches of Transformative Service Research (TSR), the study explored rural and urban perspectives of Financial Inclusion, an enabler of the attainment of the 2030 Sustainable Development Goals, in an emerging economy. A cross-sectional survey was conducted in one of the largest cities in Africa with specific emphasis on an urban and a rural population. The urban sample was obtained from a major urban setting in the city while the rural sample was obtained from the outskirts of the city. Non-probability sampling methods were used to derive the study sample, which included a combination of purposive and convenient sampling. The study sample was made up of 453 participants with 46.8% of them being rural residents and 53.2% being urban residents. A structured questionnaire was utilized in eliciting relevant information from the study participants. Outcomes as depicted by an empirical framework showed that residential status had a significant main effect on access to financial inclusion services, such that rural residents had limited access to financial inclusion services; while perceived cost of financial inclusion had a significant main effect on usage of financial inclusion services, such that perceptions of high cost of perceived inclusion resulted in less usage of financial inclusion services. Based on the study outcomes, financial institutions may consider rural-urban differentials in ascribing service charges for financial inclusion services to encourage usage.
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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 | 0.040 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| 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".