Ecological Modelling: Applying Computational Linguistic Analysis to the UN Secretary-General’s Speeches on Climate Change (2018–2022)
Bibliographic record
Abstract
The present study analyzes the UN Secretary-General’s speeches on climate change to investigate latent topics. The study aimed to sum up the challenges and strategies proposed by the UN. The addresses, delivered from 2018 to 2022, were retrieved from the official website of the UN. A computational technique named Latent Dirichlet Allocations (LDA) was applied to uncover the hidden topics from the corpus. The present study underpinned Computational Grounded Theory (CGT) as the theoretical framework for the analysis. The results revealed multiple topics such as renewable energy, the effects of climate change, proposed action plan, climate change disasters, mitigation strategies, and global food insecurity. The study is significant in the sense that it provides insightful directions to overcome the threat of climate change. La présente étude analyse les discours du Secrétaire général des Nations unies sur le changement climatique afin d'étudier les sujets latents. L'étude vise à résumer les défis et les stratégies proposés par l'ONU. Les discours, prononcés entre 2018 et 2022, ont été récupérés sur le site officiel de l'ONU. Une technique informatique appelée Allocation de Dirichlet Latent (en anglais Latent Dirichtlet Allocations ou LDA) a été appliquée pour découvrir les sujets cachés du corpus. La présente étude s'appuie sur la théorie computationnelle ancrée (Computational Grounded Theory ou CGT) en tant que cadre théorique pour l'analyse. Les résultats ont révélé de nombreux sujets tels que les énergies renouvelables, les effets du changement climatique, le plan d'action proposé, les catastrophes liées au changement climatique, les stratégies d'atténuation et l'insécurité alimentaire mondiale. L'étude est significative dans le sens où elle fournit des orientations perspicaces pour surmonter la menace du changement climatique.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".