Radicalization and the Origins of Populist Narratives about the Courts: The Argentinian Case, 2007–2015
Bibliographic record
Abstract
In Latin America, presidents from different ideological backgrounds have systematically attacked the judiciary in order to implement their preferred public policies. In many cases, the leaders who control the executive branch have shown an early normative opposition to the power of courts to engage in the process of judicial review. For this article, I conducted a case study of Argentina from 2007 to 2015 under President Cristina Fernández de Kirchner that showed a different pattern and dynamic. After judges started to block public policies, she challenged the conception that liberal democracies require an independent judiciary with the constitutional ability to limit the scope of action of the executive and legislative branches. This view challenged the traditional liberal-democratic conception of the judiciary as a counter-majoritarian branch. The presidential party characterized judges as an aristocratic caste who ruled against the popular will in order to protect corporations’ economic interests. Consequently, the president proposed a “democratized judiciary” in which judges rule following the “people’s will,” which meant whatever the president elected by a circumstantial electoral majority decided.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".