Penggunaan Teknologi Informasi dan Komunikasi dan Implikasinya terhadap Ketangguhan Mata Pencaharian Nelayan
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.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".