TINGKAT KESEJAHTERAAN KELUARGA PETANI KELAPA DI DESA KLABAT KECAMATAN DIMEMBE KABUPATEN MINAHASA UTARA
Bibliographic record
Abstract
This study aims to determine and see the level of welfare of coconut farming family in Klabat Village, Dimembe Sub-district, North Minahasa Regency. This research was conducted in February to May 2020. Sampling used purposive sampling method. Data collection was obtained through direct interviews with the help of a questionnaire, to 40 respondents who are the family head of coconut farming family heads, based the concepts and indicators of the 2015 version of the National Population and Family Planning Agency (Badan Kependudukan dan Keluarga Berencana Nasional or BKKBN) which consists of 5 stages of family welfare indicators. Data analysis used descriptive analysis method by making tables and percentages to explain the level of welfare of farmers' families in Klabat Village. The results showed that most coconut farming families were included in the category of prosperous families II (KS II). Another finding is that there are still families of KS I who have not been able to reach the level of welfare of KS II, because most of them are due to indicators of families of fertile age couples with two or more children using contraceptive devices / drugs, which have not yet been fulfilled. KS II families cannot become KS III families, because it is largely due to indicators of family income saved in the form of money or goods, which cannot be fulfilled yet. The KS III family cannot yet become the KS III plus family, because it is largely due to the indicators of the role in the community, which cannot yet be fulfilled.*eprm*
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".