Assessing the Implementation and Impact of Inclusivity and Accessibility in the Free State South African Banking Sector
Bibliographic record
Abstract
The implementation and impact of inclusivity and accessibility in the banking sector are crucial to the banking sector’s participation of any country in achieving the United Nations’ Sustainable Development Goals (SDGs) 1, 4, 5, 8, 10, 11, 16 and 17. This study examines the implementation and impact of inclusivity and accessibility in the South African banking sector, with a focus on the Free State province. Guided by the Sustainable Livelihoods Framework (SLF) and Institutional Theory, this research employs a quantitative, deductive approach to assess two core objectives: (1) the alignment of fintech banking practices with selected Sustainable Development Goals (SDGs), and (2) the identification of barriers to inclusivity and accessibility for women and youth. A stratified random sample of 208 banking professionals—comprising front-line employees, supervisors, and managers—was surveyed using a Likert-type questionnaire. Data were analysed using SPSS version 21. The findings reveal significant progress toward SDGs 1, 4, 5, 8, 10, 11, 16, and 17, reflected in a female-majority workforce, a youthful and educated employee base, and a nationally oriented employment strategy. These attributes signal a strong institutional commitment to inclusive growth and sustainable development. The sector also demonstrates readiness for fintech innovation, supported by high levels of training adequacy, relevance, and accessibility, indicating robust human capital and institutional adaptability to the Fourth Industrial Revolution (4IR) and AI-driven transformation. However, persistent structural barriers—particularly in leadership representation and digital access for women and youth—highlight the need for targeted policy interventions. Integrating inclusive fintech strategies, equitable training frameworks, and development programs is essential to sustaining progress and achieving the goals of the National Development Plan (NDP) and the SDGs. The Free State banking sector offers a promising model for inclusive institutional transformation aligned with global sustainability agendas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".