Desarrollo Sostenible y Equidad en el Ecuador: Propuestas para una Transformación Estructural del Modelo Económico
Bibliographic record
Abstract
This article presents a development model for Ecuador based on the principles of sustainability and equity. The goal is to address the country's economic, social, and environmental challenges. Inequality remains a significant issue, with a Gini coefficient of approximately 0.47 in 2022 (INEC, 2023). Compared with neighboring countries, Colombia recorded a Gini coefficient of approximately 0.49 in the same year, while Peru showed a slightly lower index, 0.46 (UNDP, 2023). Ecuador's economic structure, with a trade balance of USD 6,793.4 million in exports in the first quarter of 2023 compared to USD 7,287.7 million in imports (Central Bank of Ecuador, 2023), reflects a dependency that limits its resilience. The objective of this paper is to analyze how the characteristics of the Ecuadorian economic model hinder the achievement of sustainable and equitable development, proposing transformation strategies. The methodology is based on a comparative analysis of socioeconomic and environmental indicators from Ecuador and its neighboring countries, using recent official data. Proposals are explored to diversify the productive matrix, strengthen social protection, and adopt economic practices that align with the principles of Buen Vivir (Acosta, 2016). The findings seek to outline policy recommendations for a transition toward a more resilient, fair, and environmentally responsible Ecuadorian economic model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".