PANDEMI COVID-19 DALAM KEHIDUPAN SOSIAL-EKONOMI MASYARAKAT NELAYAN KELURAHAN PINANGSORI KECAMATAN PINANGSORI KABUPATEN TAPANULI TENGAH
Bibliographic record
Abstract
The COVID-19 pandemic has limited the movement of people and goods due to the stipulation of regulations by the government, namely PSBB/lockdown, resulting in limited marketing/distribution, especially fisheries. Where small fishermen can no longer sell their fish catches to market traders as usual again due to this pandemic and make fishermen in a dilemma because fishermen's income is decreasing and their needs are increasing, this is what makes fishing communities have to think and look for new jobs to be able to maintain their economy during this pandemic. The purpose of this study was to determine the social and economic conditions of the fishing community in Pinangsori Village, Pinangsori District, Central Tapanuli Regency in the midst of the Covid-19 pandemic. This research is a qualitative research using descriptive method. Data collection was carried out by means of library research, field studies in field studies including interviews, observations, and documentation. The results of the research on the subjects studied found that: the income of fishermen in the Pinangsori village decreased because during the pandemic the market traders who usually bought a lot of fish to fishermen were now reduced because the market was quiet so traders only took enough from fishermen, catches decreased, difficulties market the catch, expenses increase because the fishing communities also meet other needs such as buying their tools to catch fish, fishermen looking for new jobs such as being palm oil workers, construction workers to increase their income.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".