Analysis of Media Literacy Levels of Palm Oil Farmers in Riau Province, Indonesia
Bibliographic record
Abstract
Oil palm plantations are a potential commodity of Riau Province and the highest contributor to palm oil production in Indonesia. The development of agricultural science and technology, which is disseminated through various media, is a source of information for farmers. However, the availability of information sources does not guarantee that farmers will benefit from the information. This is influenced by media literacy from these farmers. This research aimed at analyzing the media’s literacy of oil palm farmers. The research was conducted in three districts with potential oil palm at Riau Province. They are Pelalawan District, Rokan Hilir District and Rokan Hulu District. The respondents of oil palm farmers were 185 farmers, which were selected by stratified random sampling. This research applied Likert Summated Rating Scale (LSRS) method. The results of this research are: The literacy level of oil palm farmers is in the medium level (average score of 1.72), while for three aspects which are technical skill and critical understanding are in the medium level and communication skill is basic level. Role of government and related parties are needed to help improve the literacy level of oil palm farmers. This is necessary so that the farmers together with extension workers, will be ready to face the challenge of the development of science and technology in the oil palm industry, by utilizing the development of information and communication technologies such as smartphones.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".