O uso da análise de correspondência e de cluster para a percepção das relações no comércio internacional - o caso do setor de móveis sul-brasileiro e as barreiras à Alca
Bibliographic record
Abstract
The main goal of this study is to analyze the perception of the high administration of companies in the furniture industry of Brazil's South region concerning threats of the Free Trade Area of the Americas (FTAA). Correspondence Analysis and Cluster Analyses were applied due to the use of categorical data. The main results are that the high administrations of furniture companies are favorable to a general concept of a free trade treat, but not to the immediate adoption of the FTAA. At the same time they consider that the main competitor countries in the FTAA are USA, Mexico and Canada. It was also verified that the high administrations attribute more importance to the external barriers than the internal barriers. However, it is inferred that this difference between external barriers and internal barriers is probably biased by the organizational culture of the companies that usually prefer to indicate problems of which other organizations are responsible than its own fragilities, because this would imply in changes in the company - like changes in the organizational culture, more investments, and other changes that almost always lead to short term traumas. The main barriers cited by the high administrations were: the exchange policy, the image of Brazil abroad and the image of Brazilian's furniture abroad. The variables of the so called "Brazil-cost" group such as taxes; seaport costs and cost of capital were indicated as important.
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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 | 0.023 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".