Export market orientation from brazilian coffee
Bibliographic record
Abstract
This paper aims to verify whether the Regional Orientation Index (ROI) can assist in decision making for export marketorientation for Brazilian coffee, to export the searching products from the major importers. The Decision Making Theory was adoptedand through the descriptive and quantitative method, the ROI is calculated in order to determine if Brazilian coffee exports are beingaddressed to the main importers. The data source of Brazilian green coffee used is the ALICEWEB base, linked to the Department ofCommerce (SECEX) of the Ministry of Industry and Trade (MDIC) for the period studied from 2000 to 2009 in US dollars (USD). Weobserved that coffee exports have been less directed towards countries like Canada, Netherlands, France, Italy, Belgium and Spain,with emphasis on decline of the ROI in Slovenia. The results also show that the Brazilian green coffee have been exported for Sweden,Finland, Japan, Germany, USA. The ROI shows increasing values for these regions, noting that over 40% of coffee imports arerepresented by Germany and USA, which are important markets for Brazil to follow when directing their exports. The originality ofthis study is to assist the decision makers through the ROI methodology for export market orientation for Brazilian coffee, accordingto the behavior and development of exports. This approach along with other economic indicators may indicate possibilities related tothe implementation of trade policies in order to redirect products to specific markets.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".