Using the Courts to Protect the Environment in Argentina: Accountability Pitfalls When Judges Have the Last Word
Bibliographic record
Abstract
In July 2006, 17 neighbors and health professionals living in the basin of one of the most polluted rivers in the world—the Matanza-Riachuelo—brought a case before the Supreme Court of Argentina. They claimed extensive health damages due to the level of contamination of the basin. The lawsuit was filed against the federal, provincial, and city governments, as well as 44 private companies [1]. This case study introduces readers to the growing pattern of judicialization of environmental policies. This trend was initially celebrated by many activists since a Supreme Court responsive to people’s demands and focused on protecting the environment could address long-standing policy failures of the executive and legislative branches of government. However, this case study examines two main ways in which judicialization may generate an accountability crisis for communities affected by environmental disasters. First, it raises a theoretical argument that a Court that takes on managerial functions beyond its adjudicative role distorts the normal horizontal accountability functions that are part of the division of powers between the three branches of government. Second, it empirically demonstrates that a Court’s involvement in policy formulation does not guarantee effectiveness and precludes vertical accountability, since citizens cannot vote judges out of office. The case suggests that judicializing environmental politics is fraught with risks to democratic accountability. These must be considered carefully before embracing the judiciary as a band-aid remedy to an executive branch that fails to protect people and the environment.
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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".