http://irgu.unigoa.ac.in/drs/handle/unigoa/6603 2026-09-03T03:07:58Z 2026-09-03T03:07:58Z George, V.M. http://irgu.unigoa.ac.in/drs/handle/unigoa/7940 2026-08-14T10:03:44Z 2026-01-01T00:00:00Z

Interpretation of Space and Understanding of Self: Reading Foucault and Ricoeur in One Frame George, V.M. This paper explores how the interpretation of space opens possibilities for self-understanding in the later works of Michel Foucault and Paul Ricoeur. First, the thinkers' projects in post-structuralist hermeneutics of the subject/self are situated in the aftermath of the "overthrow of the Cogito" by the "masters of suspicion." Next, an analysis of space as a text-analogue is passed through to understand the self's exteriorisation of meaning in built space. Here, Foucault's history of the Western concept of space is unpacked through Stephan Gunzel's exposition, and Foucault and Ricoeur are discussed as thinkers on space. Finally, "refiguration of space" is identified as the phase in which possibilities for the revealing and transforming of the self are opened. The paper shows that "spatiality of the self" offers another point of intersection to further imaginary conversations between Foucault and Ricoeur in the context of their shared concern for the self.

2026-01-01T00:00:00Z Sawaiker, Vasudha http://irgu.unigoa.ac.in/drs/handle/unigoa/7905 2026-07-31T07:31:48Z 2025-01-01T00:00:00Z

Ethics and Social Justice Pedagogy Transformative Possibilities through a Phule Ambedkarite Philosophy of Education Sawaiker, Vasudha

2025-01-01T00:00:00Z Pereira, S.F. PaiVernekar, S.D. http://irgu.unigoa.ac.in/drs/handle/unigoa/7885 2026-06-15T10:54:12Z 2026-01-01T00:00:00Z

Ethical dilemmas and decision-making processes in healthcare rationing within resource-scarce environments: a critical analysis Pereira, S.F.; PaiVernekar, S.D. Ethical dilemmas arise in healthcare rationing when the allocation of limited resources for care and the making of difficult decisions are involved. Similar situations occur in resource-scarce environments such as public hospitals in Goa, India. In this context, allocating clinical care resources is hindered by poor infrastructure, including a lack of consistent clinical protocols and unclear ethical guidance for healthcare practitioners in cases of potentially resource-rationing decisions. A Textual-Analytic Mixed Method (TAMM) containing secondary data from books, academic texts, policy documents, and news media. The oxygen crisis at Goa Medical College was a sobering reminder of the need to develop ethical frameworks and prepare for contingencies. Health practitioners implementing informal clinical processes based on urgency rather than formal triage criteria inevitably allow for bias and moral distress. To combat these situations, health practitioners need more formal ethical training, formal triage policies, possibly multidisciplinary review boards, and decentralized supply systems. If health practitioners are relying on institutional ethics to guide them in decision-making, the institution and the conditions of care must also be strong and ethical. Only then will health practitioners be accountable for fair decisions, and people will understand the difficulty of that accountability for care-oriented decision-making during crises when resources are limited.

2026-01-01T00:00:00Z Revankar, S.S. PaiVernekar, S.D. http://irgu.unigoa.ac.in/drs/handle/unigoa/7884 2026-06-15T05:44:38Z 2026-01-01T00:00:00Z

Ethics of Artificial Intelligence Revankar, S.S.; PaiVernekar, S.D. Artificial Intelligence (AI) has rapidly become integral to decision-making systems across domains such as hiring, finance, education, and law enforcement. While Al promises efficiency and scalability, it also introduces several ethical challenges. This paper examines the problem of algorithmic bias, focusing on how AI systems could amplify structural inequalities embedded in historical data. Using the Amazon hiring algorithm (2018) as a central case study, the paper demonstrates that Al tools are not impartial but socially situated technologies. It explores demographic parity, equal opportunity, and merit-based fairness in AI resource allocation. The paper also addresses issues of opacity, accountability, and loss of human agency associated with AI systems. It concludes that without fairness-aware design, transparency, and regulatory intervention, AI risks automating discrimination at scale while undermining democratic principles and natural justice.

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