Market patterns within Indonesia's songbird trade
Bibliographic record
Abstract
Songbirds recorded in Indonesia's songbird trade are more than quarter of its known species. However, only a fraction (approx. 30 %) has demand records. This trade is driven by supply-demand interactions, however current research commonly focuses on either demand or supply, and not its interactions. Understanding interaction patterns is crucial to answering what drives supply and demand, information pivotal to curbing unsustainable trade. We developed an approach to identify these interaction patterns and predict key drivers of supply and demand. We focused mainly in Java, using existing research we identified types of supply and demand data and using field surveys we filled in data gaps. We created the trade interaction value (TIV) by matching species supply and demand data to assess how well supply meets demand. We clustered species into three groups based on their TIV then developed a model to predict the cluster species with supply only records belonged to. The first cluster had the highest demand and was dominated by species with traits important for songbird competitions. Small or large sized, endemic species, and those listed as vulnerable or near threatened were important for the second cluster. The third cluster had traits common of household ornamental species. We predicted that 7.7 % of supply-only species were in the market opportunistically, while the remaining followed one of the three existing cluster patterns. Our research is the first looking at songbird trade from both sides of the market and we hope that it can be a reference for similar approaches applied to other taxa heavily affected by unsustainable trade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".