Penggunaan Teknologi Informasi dan Komunikasi dan Implikasinya terhadap Ketangguhan Mata Pencaharian Nelayan
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 livelihood of fishermen that importantly contributes to regional and rural development in coastal areas has been influenced by climate change and other pressures. On the other hand, the development of Information and Communication Technology (ICT) can be used by fishermen to develop their livelihood resilience. This research aims to explore the use of ICT and its implication to the livelihood resilience of fishermen. This research used qualitative research design using case study in Eretan Wetan Village, Indramayu Regency. The primary data was collected through interview, situational observation, activity observation, and physical artifact observation. Secondary data was also collected as supporting data to describe research context. The analysis was done using open coding to identify themes and to develop the description of those themes. The research found that fishermen that are used to ICT can improve their access to information, enhancing knowledge, enhancing and maintaining network and cooperation, and facilitating participation in the community, and eventually experiencing learning process. By experiencing learning process, fishermen will have the capability to identify information and knowledge, capability to understand challenge and opportunity, and capability to transfer and share knowledge using ICT. Therefore, fishermen will then have the capability to diversifies operational location of fishing and source of information and knowledge which are useful to redevelop access, assets, and self organization capability. These process will be cyclic and accumulate to strengthening their livelihood resilience.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it