Petróleo e Nacionalismo na Argentina Kirchnerista (2003-2015) | Oil And Nationalism In Kirchnerist Argentina (2003 - 2015)
Bibliographic record
Abstract
RESUMO Este artigo explora a complexa relação entre os recursos de petróleo e o surgimento do nacionalismo e discurso populista na Argentina durante os anos do governo de Nestor e Cristina Fernandez de Kirchner. Analisando as dinâmicas sociopolíticas, políticas econômicas e retórica em torno do petróleo, descobrimos como o controle e a exploração desse recurso estratégico se tornaram um ponto focal para fomentar sentimentos nacionalistas. O estudo demonstra como o Kirchnerismo utiliza estrategicamente o petróleo para impulsionar suas agendas políticas e consolidar o apoio entre a população argentina, remodelando, em última instância, a identidade da nação e o cenário político. ABSTRACT This paper delves into the intricate relationship between oil resources and the emergence of nationalism and populist discourse in Argentina during the Nestor and Cristina Fernandez de Kirchner years. Analyzing the socio-political dynamics, economic policies, and rhetoric surrounding oil, we uncover how the control and exploitation of this strategic resource became a focal point for fostering nationalistic sentiments. The study demonstrates how Kirchnerism strategically harnessed oil to bolster their political agendas and consolidate support among the Argentine populace, ultimately reshaping the nation's identity and political landscape.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".