Perú: estable a pesar de todo
Bibliographic record
Abstract
El presente artículo de investigación busca responder una pregunta crucial en el pensamiento colectivo, ¿por qué la economía del Perú sigue creciendo y no ha sufrido de una baja significativa a pesar de las constantes crisis sociales y políticas que el país presenta? Las razones que detalla el artículo son cinco pilares fundamentales de la estabilidad del país: independencia de instituciones claves, la estrategia monetaria de la “flotación sucia”, sólidos marcos de política fiscal que aseguran las cuentas corrientes, el constante ingreso de divisas por el modelo primario exportador y, finalmente, los avances en materia de diversificación que reduce la dependencia en una sola industria. En esta investigación, también se encuentran críticas, propuestas de mejora y preguntas que pueden conllevar a futuras investigaciones en torno al tema de reflexión.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".