Papua New Guinea food price bulletin: December 2020
Bibliographic record
Abstract
Prices of major food staples in Kokopo, Lae and Port Moresby have remained broadly stable over 2019 and 2020. The exceptions are prices of sweet potatoes and cooking bananas in Port Moresby, which declined between mid-2019 and the third quarter of 2020.\nPer kilogram prices of vitamin-dense foods such as broccoli, carrot and karakap are typically higher than prices of starchy staples. Prices of both carrots and broccoli in Lae rose in mid-2020, but have declined in the last quarter of 2020 and October prices were close to their price levels in late 2019.\nMore consistent and timely price data collection and database management is necessary for informative food market analysis. Price data reported in this bulletin by crop and market is limited to 10 observations (at most) out of a possible of 24 fortnights in 2019. The rate of price data reporting has been lower in 2020, in part due to Covid-19 related disruptions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.154 | 0.094 |
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".