Competitiveness Overview of Four Brazilian Non-timber Forest Products
Bibliographic record
Abstract
This study aimed to analyze the Brazilian competitiveness in the world market of the main non-timber forest products (NTFPs) exported by Brazil during the subperiods from 2006 to 2010, and from 2011 to 2016. The products were selected based on their relevance in the Brazilian NTFP export. In order to analyze competitiveness, we used the competitiveness matrix, which is given by the performance point of view. In the construction of this matrix, the vertical axis was represented by the Revealed Symmetric Comparative Advantage index while the horizontal axis was represented by the growth rate. The results showed that natural rubber was in the “missed opportunities” quadrant in the first period and in the “retreat” quadrant in the second period analyzed. On the other hand, honey, mate and cashew nut were positioned in the “optimum” sector in both periods, although cashew nut had showed a decrease both in the world growth rate and in the RSCA in the second period studied. In the final analysis, we concluded that Brazil is competitive in exports of honey and mate, it has been losing competitiveness in exports of cashew nuts, and is in decline as regards natural rubber exports.
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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 | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".