TRADITIONAL FINANCIAL PRACTICES VS. MODERN BANKING AMONG TRIBALS IN MANANTHAVADY TALUK: A COMPARATIVE STUDY
Bibliographic record
Abstract
Abstract : This study explores the contrast between traditional financial practices and modern banking systems among tribalcommunities in Mananthavady Taluk, Wayanad District. With the increasing integration of digital financial services in rural areas,understanding the preferences, accessibility, and challenges of these communities is essential. This comparative study seeks toexamine the utilization, benefits, and limitations of both financial systems among the tribal population. Using primary data collectedthrough a structured questionnaire, the study investigates how traditional practices like community-based lending, informal savingsgroups, and barter systems compare with modern banking services such as savings accounts, mobile banking, and digitaltransactions. The research also aims to identify the factors influencing the adoption of modern banking, such as digital literacy, trustin financial institutions, and accessibility of banking services. Through this study, the researcher aims to contribute to theunderstanding of financial inclusion in tribal regions and suggest strategies for promoting the adoption of formal banking serviceswhile respecting traditional financial practices.Index Terms: Traditional Financial Practices, Modern Banking, Tribal Communities, Financial Inclusion, Digital Literacy,Financial Security, Accessibility, Wayanad District
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".