Twitter Analysis of Collective Action of OECD Countries Against Climate Crises
Bibliographic record
Abstract
A crisis is conceptually defined as an unexpected situation requiring an urgent solution. The global climate crisis is especially significant, threatening the stability and livability of the world. Building on this conceptual framework, this study aims to explore the dimensions of stakeholder interactions to highlight the need for collective efforts to combat the climate crisis. Twitter (now known as X) interactions between climate and environment ministries in OECD countries were analyzed in R using graph theory. The study found that the United States ministry was the most followed. As the largest network node, Canada acts as both a gateway and hub. As a central player, Canada receives and disseminates information and possesses a high potential for interaction. France is connected to influential nodes and is therefore a leader in the chain of influence. Germany is a strong center of stability and acts as a leader/bridge node. Norway, Spain, and Greece are considered to be peripheral nodes. Furthermore, the network had a medium-strength community structure divided into four parts. These results highlight that interactions among OECD countries are insufficient and that climate and environment ministries can facilitate global cooperation to combat the climate crisis. Strengthening nodal connections is a recommended policy step to translate these results to real-life and industry-specific contexts.
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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".