Bibliographic record
Abstract
This book addresses political knowledge of climate change and its relation to labelling people affected by climate change, either as ‘climate refugees’ or as ‘climate change-induced displaced people or migrants’. By questioning the knowledge of climate change and subsequent labelling of people, this book will spark debate in studies of global climate politics and transnational policy networks. Rather than considering the issue of climate change as a given phenomenon, the author explores how the politicized knowledge of climate change has been produced in international negotiations and how that knowledge is transmitted from global forums to local country levels via climate change action plans and resilience projects. This book introduces the concept of multi-scalar knowledge brokers (MKBs) – individual actors who work at multiple levels (local, national, and international) to transmit the knowledge of climate change from global level to local level. The author uses the primary case study of Bangladesh to demonstrate how the dominant actors in global climate politics – the Intergovernmental Panel on Climate Change (IPCC), the United Nations Framework Convention on Climate Change (UNFCCC), and the World Bank, as well as the USA and the UK – interact with the government and local NGOs in Bangladesh regarding transmitting the knowledge of climate change, labelling the uprooted people, and implementing resilience projects. This book will be of interest to students, scholars, and practitioners of international relations, environmental politics, climate change studies, political ecology, political geography, and migration and displacement studies. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license. Thanks to the support of libraries working with Knowledge Unlatched www.knowledgeunlatched.org
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".