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 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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".