Institutional Contexts and Policy Discourses: A Case of Water Quality Governance in Lake Erie Basin
Bibliographic record
Abstract
There is growing recognition that the effects of discourse in shaping environmental policy are nested within broader institutional contexts. Consequently, over the last decade there have been increasing efforts by institutionalism scholars to theorize the link between discourses and institutions. This emerging ‘discursive institutionalism’ perspective considers discourse not only as an ensemble of ideas and their expression in language, but also takes into account the institutional contexts in which discourses emerge and are institutionalized in social practices. The application of this perspective in the context of resource governance has mainly focused on how dominant discourses become institutionalized into regulatory frameworks. However, the converse scenario, whereby the institutional context shapes the very nature of the discourse itself, has received much less attention in the scholarly literature. In this study, we employ the discursive institutional perspective to better understand the policy processes in the province of Ontario and the state of Ohio regarding the problem of eutrophication in Lake Erie, shared between Canada and the United States. Data collected through interviews, documentary sources, the news media and other relevant sources was analyzed with a process tracing approach. Results show that the federal and provincial/state level institutional arrangements in the two regions have influenced the nature of the ideational and interactive dimensions of discourse differently in the context of developing Domestic Action Plans (DAP) to address the eutrophication problem. Divergences in policy discourses revealed in the analysis show how different institutional contexts acted as filters for the varying cognitive and normative aspects of the policy discourses ultimately adopted in the DAPs. These differences may shape the relative effectiveness of achieving nutrient runoff reduction targets that initially set in motion the development of the DAPs themselves.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".