Explaining political polarization in environmental governance using narrative analysis
Bibliographic record
Abstract
Research into formation of environmental narratives can explain the process of political polarization in environmental governance, or perhaps more constructively, how to avoid it. To do so, we must broaden narrative analysis to include the evolution of relationships between environmental norms in a community and the changing positionality of the researcher. I show how this may be done, by focusing on river governance in post-Tropical Storm Irene New England, USA. The storm left residents in the region bitterly divided over how a river should be governed. Relying on interviews, newspaper articles, and judiciary and town hall proceedings, I show that two narratives coevolved from norms of vulnerability and stewardship as different groups vied for power in river governance. As they did so, the community became polarized as the newer, stewardship-based narrative gained legitimacy by problematizing traditional environmental norms. In response, community members who saw the river as dangerous and the town as vulnerable defended these norms by problematizing the new narrative. Through an iterative process, the different environmental narratives became increasingly relative as each attempted to dictate governance. Ultimately, the narratives became problematized reflections of one another. This process undermined the possibility of compromise or novel governance schemes that may have incorporated different environmental norms. To avoid polarization, researchers must at one time position themselves within the political process but take care to study how this position changes governance.
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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 | 0.000 | 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 | 0.000 |
| 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".