Damned If You Do, Dammed If You Don't: Debates on Dam Removal in the Swedish Media
Bibliographic record
Abstract
Dam removal is an increasingly common practice.Dams are removed for various reasons, with safety, economics, and ecosystem restoration being the most common.However, dam removals often cause controversy.Riparian land owners and local communities often have a negative view of removal, and their reasons vary.It may be the loss of recreational benefits such as swimming and boating, loss of cultural and historical context tied to the dam, or fear that removal may have a negative effect on aesthetic values.Because controversies are often picked up by local media, and media in itself is an important channel to build support around a cause, the way in which dam removals are reported and discussed in the media is likely to influence the debate.Here, we examine the ways in which proponents and opponents of dam removal frame the services provided by two contrasting ecosystems, i.e., an existing dam and the potential stream without a dam, by performing a media discourse analysis of the reasons given for removal and the reasons presented for the dam to remain in place.Our source material includes Internetbased newspaper articles and their associated public comments in four dam removal controversies in Sweden.Our analysis indicates that public opposition is not based on knowledge deficiency, where more information will lead to better ecological decision-making, as is sometimes argued in dam removal science; it is instead a case of different understandings and valuation of the environment and the functions it provides.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.026 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".