Does Frontier Technology Readiness Affect Vulnerability to Climate Change?
Bibliographic record
Abstract
ABSTRACT The overall goal of this research is to assess the impact of Frontier Technology Readiness on vulnerability to climate change. From a sample of 120 developing nations from 2008 to 2019, we use a simple panel specification to first account for cross‐sectional dependence and autocorrelation, which are estimated using Driscoll‐Kraay's OLS and Generalized Least Squares (GLS). Second, we use the instrumental variables method to adjust for any potential endogeneity in our association. At the conclusion of our studies, our findings show that preparation for advanced technologies has a negative influence on sensitivity to climate change; in other words, an increase in Frontier Technology Readiness reduces vulnerability to climate change via technological effect. This effect is particularly strong in middle‐ and high‐income countries. When we use different ways to control endogeneity, our results stay consistent. Related Articles Myers, N., A. Pendergast, A. Tidmore, et al. 2025. “Roles in Resilience: The Intersection of Genetic Counseling, Policy Advocacy, and Community Resilience.” Politics & Policy 53 no. 2: e70023. https://doi.org/10.1111/polp.70023 . von Malmborg, F. 2023. “Combining the Advocacy Coalition Framework and Argumentative Discourse Analysis: The Case of the ‘Energy Efficiency First’ Principle in EU Energy and Climate Policy.” Politics & Policy 51 no. 2: 222–241. https://doi.org/10.1111/polp.12525 . Zang, X. 2021. “Environmental Accidents and Environmental Legislation in China: Evidence from Provincial Panel Data (1997–2014).” Politics & Policy 50 no. 1: 77–92. https://doi.org/10.1111/polp.12446 .
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".