'Boom' nas vendas de autoveículos via crédito farto, preços baixos e confiança em alta: o caso de um ciclo?
Bibliographic record
Abstract
A proposta deste trabalho é contribuir para o debate acerta dos primeiros efeitos da crise de crédito mundial na economia brasileira. Seus primeiros efeitos já podem ser observados nos números de outubro referentes a vendas de veículos, crédito ao consumidor e expectativas para o futuro próximo. Para esta análise, lançou-se mão da ferramenta dos Vetores Auto-Regressivos (VAR) de forma a estimar os impactos de uma alta (ou queda) do crédito, dos preços e da confiança nas vendas de automóveis. A conclusão é de que as variáveis escolhidas são relevantes, sobretudo o volume de crédito. Este escasseando, as vendas de automóveis não só devem, como já estão, de fato, em fase de contração.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.014 |
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; both teacher heads agree on what is shown here.
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".