La competitividad del sector minero metalífero argentino en perspectiva: una comparación con Australia y Canadá | The competitiveness of the Argentine metal mining sector in perspective: a comparison with Australia and Canada
Bibliographic record
Abstract
Desde una perspectiva extractivista, la competitividad de una jurisdiccion puede ser entendida como su capacidad de atraer capital para desarrollar el negocio minero. En las ultimas decadas, la proliferacion de conflictos socioambientales asociados a grandes proyectos mineros ha llevado a este sector a prestar especial atencion a factores sociales y ambientales. Mediante una revision bibliografica de tipo exploratoria, en este trabajo se analiza la evolucion historica del concepto de competitividad y se realiza un analisis de sus principales determinantes comparando el caso argentino con el desempeno de Australia y Canada. Los resultados revelan que, para incrementar la competitividad de su sector minero metalifero, Argentina necesita mejorar su estabilidad politica y macroeconomica, requiere de un fortalecimiento institucional que incluya espacios formales de dialogo y construccion de consensos, como tambien invertir en desarrollo de factores que aumenten la productividad, por ejemplo en innovacion y en la calificacion de la mano de obra. Otro aspecto fundamental a mejorar son los factores que condicionan la licencia social para operar, entre ellos la calidad de las relaciones empresa-comunidad y gobierno-comunidad (transparencia de informacion sobre controles ambientales, estado del ambiente), los argumentos detras de las posturas criticas sobre el caracter extractivista del sector, como tambien la comprension del concepto de riesgo ambiental. From an extractivist perspective, the competitiveness of a jurisdiction can be understood as its ability to attract capital to develop the mining business. In recent decades, the proliferation of socio-environmental conflicts associated with large-scale mining projects has led this sector to pay special attention to social and environmental factors. Through an exploratory literature review, this paper analyses the historical evolution of the competitiveness concept and examines its main determinants, comparing the Argentine case with the performance of Australia and Canada. Findings reveal that, in order to enhance the competitiveness of its metal mining sector, Argentina needs to improve its political and macroeconomic stability, it needs to strengthen its institutions by creating formal spaces for dialogue and consensus building, and needs to invest more in factors that increase productivity, such as innovation and the qualification of its mining workforce. Another fundamental aspect to improve are the factors that condition the social license to operate among them, the quality of the company-community and government-community relationship (transparency of information on environmental state controls, status of the environment), the arguments behind critics on extractivism, as well as the understanding of the concept of environmental risk.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".