Rantai Pasar Ikan Cakalang Asap Pada Masa Pandemi Covid-19 Dan New Normal Di Kota Jayapura
Bibliographic record
Abstract
The conditions of the Covid-19 pandemic that occurred in 2019-2021 affected the marketing system for Smoked Skipjack. Therefore, the research will be conducted to find out the marketing channels and marketing margins of Smoked Skipjack during the Covid-19 pandemic and the new normal. The research lasted for 6 months at the Mama-mama Papua Market, Hamadi Market, and Cikombong Market. This research method is purposive sampling and be analyzed using descriptive qualitative and quantitative. Based on the research results, it was found that the marketing channel started with fishermen in Dock 9, Jayapura City, who sold their catch to collectors, then collectors sold it to traders who processed fish into smoked fish and smoked fish traders sold it directly to consumers. During the Covid-19 pandemic, the market that had the lowest marketing margin was the Cikombong Market and the highest margin was the Hamadi Market. During the new normal, the highest marketing margin occurred at the Hamadi Market and the lowest was the Cigombong Market, followed by the Mama-Mama Papua Market. During the Covid-19 pandemic and the new normal, efficient markets were the Papua Mama-mama Market, Hamadi Market and Cigombong market, because had <5% efficiency.Then during the new normal, marketing efficiency at Hamadi Market, Papua Mama-mama Market and Cigombong Market were efficient because it had <5% efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".