PERAN TEKNOLOGI INFORMASI DAN KOMUNIKASI PADA PROGRAM KEMITRAAN PT TANIFUND MADANI INDONESIA (TANIFUND)
Bibliographic record
Abstract
The agricultural sector is one of the highest contributors to Indonesia’s GDP, even during the COVID-19 pandemic. The irony is that based on Indonesian Central Bureau of Statistics data, at least 25.14 million Indonesian were below the poverty line, with 15.15 million lives in rural areas and the majority of whom worked in the agricultural sector. TaniFund is a startup company with a vision to improve the welfare of farmers by utilizing information and communication technology (ICT). TaniFund builds partnerships with farmers in rural areas and opens access to capital through a peer-to-peer lending system. This study aims to describe the ICT in the TaniFund partnership program with farmers, using qualitative methods and a phenomenological approach through literature study, documentation, observation, and in-depth interviews. The results of the study identified the use of smartphones and internet access to support information and data exchange, communication applications, search engine sites, peer-to-peer lending systems, and long-distance remittances, as part of ICT. ICT plays a significant role as an enabler in this partnership, for TaniFund still uses several conventional approaches but it is ICT that allows the smoother, faster, more transparent, and accountable data and information exchange, also encouraging financial inclusion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".