A Typology of Climate Obstruction Discourses: Phenomenon, Action, Source
Bibliographic record
Abstract
Climate inaction has traditionally been attributed to skepticism and denial. However, strategies obstructing climate action have become more nuanced, shifting from direct denial to complex forms of delay. This study presents a typology of discursive strategies of climate obstruction that expands the existing literature while comprehensively classifying tactics that hinder climate action. We argue that climate obstruction better encapsulates the broad strategies used to delay climate action than climate skepticism. Our typology comprises three categories, each distinguished by the target of obstruction. First, Phenomenon Obstruction (Target 1) includes discourses to obstruct the fact that climate change is occurring and human-caused as well as the severity of its impacts. Second, Climate Action Obstruction (Target 2) encompasses discourses targeting solutions to tackle climate change, such as promoting unproven solutions or greenwashing, shifting responsibility, casting doubt on climate policy productivity, looking for policy perfectness, and appealing to cultural and partisan identity to oppose climate action. Last, Source Credibility Obstruction (Target 3) undermines the credibility of climate actors or climate research, pertaining to its method and data as opposed to questioning the existence of the phenomenon itself (Target 1), and casting such actors as conspirators. Based on a review of 138 scholarly publications, this framework equips scholars to analyze how these strategies manifest across political discourse, news media, and social media.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| 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".