Mapping Political Coalitions in the Public Debate on Small Modular Reactors in Argentina and Canada
Bibliographic record
Abstract
Small modular reactors (SMRs) are being actively pursued globally as part of decarbonization strategies in response to threats from climate change. This dissertation examines how public debates about SMRs are structured and have evolved in Argentina and Canada, two SMR-developing countries with contrasting media systems and federal institutions. This dissertation addresses four questions: (1) who participates in these debates, and how do actors cluster into coalitions supporting or opposing SMRs; (2) which narratives are constructed to justify those positions, and how do they change across time, platforms, and countries; (3) how polarized the discursive arenas are, and which concepts generate the most division; and (4) how platform logics and transnational linkages (such as shared frames and the role of international organizations) shape coalition structures and narratives. Using discourse network analysis (DNA) of newspaper articles and posts on X (formerly Twitter), combined with in-depth case studies and cross-platform comparison, stakeholder coalitions, narrative frames, and polarization are mapped over the last decade (Argentina: 2010–2022; Canada: 2014–2023). Findings show: (a) a durable pro-SMR coalition in both countries that broadened over time from industry and sub-national governments to include federal agencies, international organizations, and foreign entities, increasing influence through strategic narratives and institutionalization; (b) opposition concentrated on nuclear waste management and economic viability, with relatively stable frames that nevertheless surged episodically; (c) clear platform effects, with X/Twitter hosting larger, more actor-diverse, and more polarized networks than newspapers, amplifying “progress” narratives (e.g., milestones, permitting, partnerships) and enabling broader dissent without dislodging dominant frames; (d) in Argentina, debate around CAREM-25 (Argentina’s domestically developed SMR) exhibited cross-partisan support anchored in techno-nationalist narratives of scientific capability and industrial policy, with “steady progress” signals helping consolidate consensus; and (e) in Canada, discourse shifted from early technical considerations to climate, feasibility, economic development, and international partnerships, as a pro-SMR coalition expanded and embedded its frames in policy venues, while persistent contention remained around waste and costs. Across cases, core ideas travel transnationally but differ in salience and coalition uptake across national contexts. The dissertation contributes substantively and methodologically. Substantively, it explains how coalition coherence and sustained narrative activity underpin discourse dominance across platforms, and it identifies the “smooth/steady progress” frame as a mechanism that secures support, mobilizes resources, and raises exit costs for opponents. Methodologically, it demonstrates the value of combining DNA with cross-platform, cross-national comparison to track long-horizon policy debates using both social and traditional media text. Practically, the study highlights design levers for more inclusive energy-policy deliberation beyond industry–government dyads by bringing a wider range of actors into agenda-setting and by making narrative contestation more transparent. More broadly, the findings offer guidance for adaptive, evidence-informed governance of emerging energy technologies.
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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 | 0.001 |
| 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".