Competitiveness of the Sugarcane Cluster in Goianesia-GO, Brazil
Bibliographic record
Abstract
The present study aims to analyze the competitiveness of the chain of sugarcane cluster that is located in Goianesia (Goiás state, Brazil) and in nearby municipalities like Barro Alto, Santa Rita and Vila Propicio. It was used Michael Porter’s Diamond of Competitiveness, which lets to study the competitiveness of a company, cluster or country, by four factors: demand conditions, factor conditions, context for firm strategy and rivalry and related and supporting industries. To build the Diamond of Competitiveness was used secondary and primary information, where the latter was collected by interviewing the key actors inside and outside the Jalles Machado, central company of the cluster analyzed. In terms of results they were found several interesting aspects that affect (positively and negatively) competitiveness of the cluster. It was identified, for instance, how investment in research and development, as well as the adoption of technology, are key for the central company of the cluster to facing the physical constraints of the Goianesian soil, and how this contribute to the cluster competitiveness against other actors that are in better conditions. In addition, the cooperation between cluster’s stakeholders - which makes the central company and its partners identify together the cluster's weaknesses and work on a solution- was identified as a key factor in creating competitive advantage. The paper presents the factors that affect both positively and negatively the competitiveness of Goianésia’s cane sugar cluster, leaving available the necessary inputs for policy makers drawing strategies for improving the competitiveness of this cluster.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".