The split ladder of policy problems, participation, and politicization: constitutional water change in Ecuador and Chile
Bibliographic record
Abstract
There is debate about whether complex problems should be addressed technocratically or whether they should be politicized. While many tend to favour technocratic decision-making and evidence based policy, for others politicization of policy problems is fundamental for significant policy change. But politicization does not always lead to problem solving. Nor is it always necessary. This paper addresses the question: Under what circumstances should problems be politicized, and what is the effect of such politicization? It adds politicization, through windows of opportunity, to the split ladder of participation to assess policy change through two case studies: successful and unsuccessful constitutional change in Ecuador (2008) and Chile respectively (2022). It argues that where there is no agreement on either science or policy, politicization is required to address lack of consensus in values, but constitutional protection is needed to protect minorities and the vulnerable, their access and human right to water. De-politicization stymies policy change potentially harming democracy. This paper argues for a citizen engaged exploration of the complex problem of climate change and its impacts on water, but a targeted politicization coincident with, but developed well in advance of, windows of opportunity. Moreover, policy framing correlated with complex problems continues to be a key consideration. Furthermore, alliances of disparate actors, elections of new political leaders and considerations of property rights and justice issues are paramount. Significant constitutional policy change reflects social learning, but subsequent court actions by policy entrepreneurs is required to effectively implement this change. Framing constitutional change to protect rights to water and effect international agreements (including the Warsaw International Mechanism under the climate change regime) advances water justice and may increase success.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".