PERAN TEKNOLOGI INFORMASI DAN KOMUNIKASI PADA PROGRAM KEMITRAAN PT TANIFUND MADANI INDONESIA (TANIFUND)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it