Climate Change and the Knowledge Vacuum in India
Bibliographic record
Abstract
Human action is closely linked to many of our maladies including climate change and the present pandemic. Although, there is some degree of convergence between climate change and the pandemic, we may not witness a significant shift in global priorities with respect to health and its determinants. Apart from human role, the convergence between the two is also in terms of lack of knowledge base.[1] In India, the National Mission on Strategic Knowledge on climate change especially points out the knowledge gaps in several areas with respect to the impact of climate change including health and the fragmented nature of available knowledge in terms of people, institution and capabilities.[2] Both climate change and COVID-19 are borne out of anthropogenic factors. For instance, one can witness burning of household and roadside waste including plastic wastes in the mornings/evenings along the main roads and it is considered as a ‘’normal’ phenomenon in India. Similarly, crop residue burning during fag end of Kharif and Rabi harvesting seasons are widespread phenomena in the entire Indo-Gangetic plains. We have sufficient historical accounts now regarding climate-linked disasters and their impacts from many famines, floods etc. around the world.[3] The linkages between seasonal fluctuations in infectious diseases have also been documented but long-term trends in climate-disease associations are not available despite the creation of many models to predict them. The traditional knowledge systems are also important to be assimilated to evolve a holistic understanding regarding many such issues. Case studies support the hypothesis that community based institutions can help to strengthen the local capacities for managing resources and even disposal of wastes in a sustainable way.[4] As part of this National Mission, Climate Change Cells were set up in all states of India in order to usher the understanding of evidences and impacts of climate change, Many state governments in India have identified knowledge generation as an important component related to climate change. For instance, various agencies have been created in the state of Kerala for coordinating activities related to climate change in the state especially focusing on knowledge generation. The components include the preparation of position papers on climate change through resource institutions and consultants, preparation of consultancy reports on carbon credits, initiating study reports, climate change action plan for the state and for organising a workshop on climate change.[5] But despite such intentions, the knowledge gaps continue to exist especially on the linkages between climate change and human health except some early as well as a few recent evidences.[6,7,8] These impacts may affect those with existing respiratory diseases apart from influencing the incidence and thus prevalence of respiratory conditions. However, most of these evidences are based on assumptions and possibilities and therefore, there is a strong need for undertaking context based studies in order to develop local solutions. Some of the State-sponsored reports in India helped in saving climate change from reducing itself into mere fashionable gobbledygook at evening parties. The flood disaster which occurred in the state of Kerala in India in the year 2018 was a clear warning to take steps to close the knowledge gaps as many of the issues such as displacement of people from the habitats, and a massive outbreak of infectious diseases especially Dengue and Leptospirosis predicted by many international reports were observed here. People who built palatial houses by spending all their hard-earned money found their houses ravaged by the surging waters which even necessitated psychological counselling to get back to their normal life. The recent flash floods in several North Indian states, in Western Europe and the heat wave in USA and Canada are also eye-openers for urgent actions required for evidence building based on focused-contextual action research on infectious respiratory infections and formulation of programs. In order to avoid knowledge generation by self-styled experts and half-baked plagiarists, there is a need for original, contextual, integrated and inter-sectoral plans as well as studies which can generate evidence regarding linkages between climate change and health and strategies based on these observations. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".