75 Days Mains Revision Plan 2024 – GS3 Full Length
Q1: India’s achievement in vaccine development and distribution is a testament to its strength in science, technology, and resilience. Elucidate. (10M)
Key Demand of the Question:
Analyze how India’s achievements in vaccine development and distribution highlight its capabilities in science, technology, and resilience. Discuss the role of indigenous vaccine development, global collaboration, manufacturing strength, affordable pricing, and logistical efficiency. Also, examine how India’s response to the COVID-19 pandemic demonstrated its ability to overcome challenges and maintain public health.
Structure of the Answer:
Introduction: Start by mentioning India’s significant achievements in vaccine development and distribution, especially during the COVID-19 pandemic.
Body:
- Indigenous Development and Global Collaboration: Discuss India’s development of vaccines like Covaxin and ZyCovD, and collaboration on Covishield.
- Vaccine Manufacturing and Affordability: Highlight India’s capacity to produce billions of doses and its role in global vaccine distribution.
- Logistics and Supply Chain Management: Explain India’s logistical capabilities, such as cold chain management and distribution networks.
- Resilience: Analyze India’s ability to overcome public health challenges, including vaccine hesitancy and medical supply shortages during the pandemic.
Conclusion:
Summarize how these achievements underscore India’s strength in science, technology, and resilience, contributing to global health security.
Introduction:
A vaccine is a biological preparation that provides active acquired immunity to a particular infectious or malignant disease. Vaccination is a proven and one of the most cost-effective child survival interventions.
Body:
India’s achievement in vaccine development and distribution:
- Indigenous Development: India has developed two COVID-19 vaccines – Covaxin and ZyCovD – indigenously two Indian companies.
- Global Collaboration: India has collaborated with other countries and organizations to develop vaccines.
- For example, Covishield was developed in collaboration with Oxford University and AstraZeneca.
- Vaccine Manufacturing: India has a strong vaccine manufacturing industry, which has the capacity to produce more than 2 billion doses annually.
- India’s capability in vaccine manufacturing has played a significant role in making vaccines available to other countries.
- Affordable Prices: India has been able to produce vaccines at an affordable price. This has made vaccines accessible to people.
- Universalisation: India’s universalisation immunization program (UIP) provides free vaccines to beneficiaries for over 11 diseases.
- Intensification: India’s vaccine distribution is being intensified under the mission Indradhanush to achieve universal vaccination in non-attainment areas.
- According to the recent NFHS-5 report, the proportion of children who are ‘fully vaccinated’ has reached 77% from 62% in the previous report.
- Tech–enablement: Initiatives like cold chain development, digital e-VIN portal et al have boosted vaccine logistics and distribution.
A testament to its strength in science, technology, and resilience:
- Science and technology:
- India has expertise in both traditional and modern vaccine technologies, such as inactivated vaccines, recombinant protein vaccines, and mRNA and vector-based vaccines.
- India has a history of successful vaccine development, including the development of the Rotavirus vaccine, which has been adopted by the World Health Organization (WHO) and is used globally.
- India has a well-established regulatory system for vaccine development and approval, which has helped to ensure the safety and efficacy of vaccines.
- Logistics and supply chain management:
- India has a vast network of healthcare infrastructure, including primary healthcare centres, community health centres, and district hospitals, which have played a crucial role in vaccine distribution.
- India has a well-established distribution system that has been able to quickly reach remote and rural areas of the country.
- During the COVID-19 pandemic, India was able to rapidly scale up its vaccine production and distribution, leading to a significant increase in the number of vaccinations administered.
- Resilience:
- India has faced numerous public health challenges, such as outbreaks of polio, measles, and other infectious diseases, which have required a strong response from the healthcare system.
- During the COVID-19 pandemic, India faced shortages of medical supplies such as oxygen and PPE but was able to rapidly increase domestic production to meet the demand.
- India also faced significant vaccine hesitancy, particularly in rural areas, but was able to overcome this challenge through public education and outreach programs.
- India’s experience in responding to public health challenges has helped to build resilience in its healthcare system and ensure that it is prepared for future challenges.
Conclusion:
Thus, India’s achievement in vaccine development and distribution is a testament to its strength in science, technology, and resilience. These strengths have helped India to develop and distribute vaccines efficiently and effectively and will continue to play a crucial role in the country’s efforts to promote public health and well-being. India must work faster towards universal immunization status.
Q2: Explain the science behind the working of generative Artificial Intelligence. Also, describe how it can be effectively utilized for addressing socio-economic issues in India. (15M)
Key Demand of the Question:
Explore the underlying science of generative AI, including neural networks and machine learning algorithms, and analyze its potential applications for socio-economic development in India. Discuss its uses in healthcare, education, and poverty reduction, while also addressing potential challenges like bias, privacy, and resource constraints.
Structure of the Answer:
Introduction:
Define generative AI and briefly mention its significance in creating new content based on learned patterns.
Body:
- Science of Generative AI: Explain the role of neural networks, training data, loss functions, and Generative Adversarial Networks (GANs) in generative AI.
- Applications in Healthcare: Discuss how generative AI can help in early disease detection, drug development, and personalized medicine.
- Applications in Education: Highlight the potential of generative AI in creating personalized learning programs and educational resources.
- Applications in Poverty Reduction: Describe how AI can analyze data to design targeted poverty alleviation programs and predictive models.
- Challenges and Limitations: Address issues like bias, privacy concerns, data availability, and the high costs associated with implementing generative AI.
Conclusion:
Emphasize the transformative potential of generative AI for socio-economic development in India, while noting the need for ethical considerations and infrastructure support.
Introduction
Generative Artificial Intelligence (AI) is a type of AI that involves using algorithms and models to generate new data, images, text, or other content. It is also known as generative modelling or generative adversarial networks (GANs).
Generative AI differs from other types of AI, such as supervised learning or reinforcement learning, in that it does not require explicit input or output data to be provided during the training phase. Instead, the model is able to generate new content on its own, based on the patterns it has learned from the data.
- Applications of generative AI – Creating realistic-looking images or videos, such as in deep fakes or computer-generated graphics for movies and video games, Natural language processing, to generate human-like text or speech, and in music composition, to create new melodies and harmonies.
The science behind the working of Generative AI
- Generative Artificial Intelligence (AI) is a complex and evolving field that involves the use of neural networks and machine learning algorithms to generate new content based on existing data.
- The fundamental idea behind generative AI is that it can learn to create new content that is similar in style or structure to existing data. This is achieved by training a model on a large dataset, such as images, videos, or text.
- Neural Networks: In generative AI, neural networks are used to learn the patterns and structures of existing data, and to generate new content based on those patterns.
- Training Data: To train a generative AI model, a large dataset of existing content is typically required. For example, to generate new images, a model might be trained on a dataset of thousands or millions of images.
- Loss Functions: The loss function calculates the difference between the generated content and the actual data, and provides feedback to the model on how to adjust its parameters in order to generate more accurate content.
- Generative Adversarial Networks (GANs): GANs are a popular type of generative AI model that consists of two neural networks: a generator network and a discriminator network.
- The generator network generates new content based on the patterns it has learned from the training data, while the discriminator network evaluates the quality of the generated content and provides feedback to the generator on how to improve it. This feedback loop helps the generator to improve its ability to generate new content that is similar to the training data.
- The field is constantly evolving, as new techniques and algorithms are developed to improve the accuracy and creativity of generative AI models
Potential applications of generative AI for addressing socio-economic issues in India
- Healthcare: Generative AI can be used to analyze healthcare data to develop new treatments and drugs for various diseases.
- For example, It can be used to analyze medical imaging data to identify early-stage cancers or to predict the progression of diseases like diabetes or Alzheimer’s. Additionally, It can be used to develop personalized medicine that is tailored to a patient’s unique genetic makeup.
- Education: Generative AI can be used to develop personalized learning programs that are tailored to individual student’s learning styles and abilities.
- For example, It can be used to analyze student performance data to identify areas where a student needs additional support and to create customized lesson plans that are tailored to their needs. It can be used to develop educational resources, such as textbooks or instructional videos, that are accessible to students in remote or underprivileged areas.
- Poverty Reduction: Generative AI can be used to analyze poverty data to identify patterns and trends that can be used to develop targeted poverty reduction programs.
- For example, generative AI can be used to analyze census data to identify areas where poverty rates are high and to develop programs that provide job training, education, and financial assistance to people in those areas.
- Additionally, generative AI can be used to develop predictive models that can help identify people who are at risk of falling into poverty and to develop early intervention programs to prevent it.
Potential challenges and limitations of utilizing generative AI for socio-economic issues
- Bias: This can occur when the training data used to develop the generative AI model is biased in some way, which can lead to the model generating biased or unfair outcomes.
- For example, if a model is trained on data that only includes patients from certain demographics, it may generate treatment recommendations that are biased towards those demographics, while ignoring the needs of other patients.
- Privacy: Generative AI models often require large amounts of data to be trained on, and this data may include sensitive information about individuals. If this data is not properly secured or anonymized, it can lead to privacy violations and breaches.
- Limited Data Availability: In certain areas, such as rural or underdeveloped regions, there may be limited data availability.
- Cost: Developing and implementing generative AI models can be expensive, requiring significant investment in computing resources, data acquisition, and technical expertise. This can limit the ability of organizations and governments to leverage generative AI to address socio-economic issues, particularly in resource-limited areas.
Conclusion
Overall, generative AI has the potential to revolutionize a wide range of industries and applications, by enabling machines to generate new and creative content that was previously thought to be the exclusive domain of human creativity.
ETHICS
3Q. Formation of attitudes is an essential part of the process of our socialisation. Elucidate [10M, 150words]
Key Demand of question:
Explain how attitude is essential part of socialisation process with example
Structure of the answer:
Introduction: Define attitude.
Body:
- First, write how attitude is essential part of socialisation.
- Then briefly explain how to inculcate attitude in individual.
Conclusion:
Conclude by highlighting the significance of attitude.
Introduction:
Attitude refers to a learned predisposition to respond in a consistently favorable or unfavorable manner towards a particular object, person, or situation. It is a crucial aspect of human behavior, influencing how individuals perceive and interact with the world.
Body:
Attitude is an essential part of socialization because:
- Internalization of societal values: Through socialization, individuals internalize societal norms and values, which form the foundation of their attitudes.
E.g. Learning to respect elders through touching feet.
- Influence of family, peers, and media: Family, friends, and media play a crucial role in shaping attitudes by providing information, experiences, and examples that individuals observe and adopt.
E.g. Moral teaching by family
- Guiding social behavior: Attitudes serve as a framework for decision-making and behavior in social situations, influencing how individuals react.
E.g. Inculcate favour and disfavour towards certain action.
- Formation of identity and group affiliations: Attitudes contribute to the development of personal identity and help individuals align with certain groups.
E.g. Making friends and colleagues.
- Shaping social roles and expectations: Attitudes influence the roles individuals assume in society and the expectations placed upon them by others.
Inculcating attitudes in individuals:
- Family, education, and media as agents: These are primary agents of socialization that instill attitudes by teaching values, providing knowledge, and setting examples.
E.g. Grandparent’s bed stories.
- Role models and reinforcement: Positive reinforcement from role models, such as parents, teachers, or public figures, helps solidify attitudes.
E.g. APJ Abdul Kalam role model.
- Repeated exposure to values: Regular exposure to specific values through education, media, and social interactions reinforces particular attitudes.
E.g. Movies and TV series with No Tobacco ads.
- Educational programs promoting ethical attitudes: Formal education often includes programs designed to foster ethical attitudes, such as respect, responsibility, and empathy, which are essential for social harmony.
E.g. Karnataka Navu Manujaru program.
- Social experiences reinforcing cooperation: Social interactions that emphasize collaboration, tolerance, and mutual respect reinforce positive attitudes.
Conclusion:
The formation of attitudes is a foundational element of socialization, shaping individuals’ behavior and interactions within society. Cultivating positive attitudes is essential for fostering a cohesive and harmonious social environment.
4Q. Explain the following terms with example:
-
- Operant conditioning
- Cognitive dissonance
- Reactance [10M, 150words]
Key Demand of question:
Explain the above terms and write example for each.
Structure of the answer:
Introduction:
Directly you can address the question.
Body:
- Your answer should have following structure for each term:
- First, define the term given.
- Next, write example for each term.
- Lastly, explain the significance.
Conclusion:
You can conclude with general conclusion.
Introduction:
The terms operant conditioning, cognitive dissonance, and reactance are psychological concepts that describe different aspects of human behavior and decision-making processes.
Body:
- a) Operant conditioning: Operant conditioning is a learning process where behaviors are influenced by the consequences that follow them. Positive consequences reinforce and increase the likelihood of the behavior, while negative consequences weaken it.
E.g. If a student receives praise for completing homework on time, they are more likely to repeat this behavior.
- Significance: Operant conditioning is crucial in shaping behavior through rewards and punishments, making it widely used in education, parenting, and workplace management.
- b) Cognitive dissonance: Cognitive dissonance occurs when an individual experiences discomfort due to holding contradictory beliefs, attitudes, or behaviors, leading them to modify one to reduce the discomfort.
E.g. A person who values health but smokes may feel cognitive dissonance.
- Significance: Understanding cognitive dissonance helps in recognizing how individuals reconcile conflicting beliefs and actions, often driving change in behavior or attitude.
- c) Reactance: Reactance is the psychological response to a perceived threat to one’s freedom or autonomy, leading the individual to resist persuasion and defend their existing attitudes.
E.g. When a teenager is told not to wear a certain type of clothing, they may react by deliberately wearing it more often, asserting their independence.
- Significance: Reactance highlights the importance of respecting individual autonomy in persuasion efforts, as perceived threats can lead to resistance rather than compliance.
Conclusion:
These psychological concepts operant conditioning, cognitive dissonance, and reactance offer valuable insights into human behavior, guiding strategies in education, communication, and behavior modification.
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