Climate Change and the Knowledge Vacuum in India
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,016 | 0,026 |
| Communication savante | 0,020 | 0,010 |
| Science ouverte | 0,002 | 0,020 |
| Intégrité de la recherche | 0,002 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».