Tackling the Scourge of Climate Change: Pakistan’s Engagements in Climate Diplomacy
Bibliographic record
Abstract
Climate change is one of the most destructive phenomena affecting and shaping regional and global landscapes.Its effects are wide-ranging, permeating important domains like agriculture, economics, inter-state relations, and armed conflicts, to name a few. All this makes climate change a major security threat, one that cannot be navigated without concerted global efforts. This realization has engendered the concept and practice of climate diplomacy. This paper traces the genesis and evolution of climate diplomacy, casting light on how arrangements like the Montreal and Kyoto Protocols, and the Paris Agreement came to the fore. The paper then looks at Pakistan’s engagements in climate diplomacy as part of the country’s overall climate action during the tenure of former Prime Minister, Imran Khan. The paper’s research methodology is centered on an analysis of secondary sources as well as a structured interview with one of the key ministers who dealt with climate change-related issues under the Khan government.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".