This study examines the intersection of popular music/culture, social movement and protest by analysing the numerous protest music produced and performed by Fela Anikulapo Kuti, Africa’s most iconic resistant artist of the twentieth century. It engages the core questions of right, injustice and inequality that have manifested in Africa/Nigeria’s underdevelopment since the Union Jack was lowered in 1960. It argues that Fela’s music did have obvious impact on Nigerian youth and the working class who attempted to revise or renegotiate their relationship with the Nigerian state. Yet, it posed hitherto unanswered questions of the changing meaning of social movement in relation to artistic production -- an aspect of peace studies that scholars have completely overlooked. It concludes that as people reconfigure social relations from one stage to another in their life, their engagement with the State and the social meaning attributed to social justice, which Fela’s music emphasised, also change. Thus, popular consciousness shaped by resistant music is not immutable to nonviolent social protest. Rather, it continued to change as individuals and groups reconstitute their relationship with the society, and as their social status transformed in accordance with the acquisition of better education, wealth/resources, among other significant elements that shape human’s consciousness.

Coca-Cola is a carbonated beverage created by The Coca-Cola Company in Atlanta, Georgia, United States of America (USA) which leadership in the soft drink business was achieved after development of advertising programs. Phonecards are items collected by people worldwide. Telecommunication companies along with The Coca-Cola Company displayed Coca-Cola advertising on phonecards. The objective was to illustrate The Coca-Cola Company advertising history through Coca-Cola phonecards. Single phonecards and those in the form of sets and puzzles, besides phonecard folders were used in the study. The number of Coca-Cola phonecards ever produced per country and continent was listed. Coca-Cola advertising slogans were extensively displayed on phonecards, including “Always in Motion”, “Delicious & Refreshing”, “Drive Refreshed”, “Enjoy”, “Have a Coke”, “Refreshing”, “Refresh Yourself”, “Talk about Refreshing”, and “What I Want is a Coke” from Japan as well as “Refreshment Right Out of the Bottle” and “Always Together” from Hungary and Indonesia, respectively. Japan had the highest number of Coca-Cola phonecards ever produced, 584, followed by USA, 441, China, 95, Germany, 51, and Australia, 41. Asia had the highest number of Coca-Cola phonecards ever produced, 760, followed by America, 519 and Europe, 211. The Coca-Cola Company history was successfully illustrated through advertising phonecards.

In the first part, this article deals with the idea of supporting Moral Intuitionism by drawing an analogy with conceptual mathematical knowledge. The analysis shows that arguments of pro and contra to the above idea are rather aimed toward assumptions and expectations of moral epistemologists; the arguments miss the essence of mathematical conceptual thinking. The image of mathematical thinking exemplified in the epistemological discussion is probably afflicted by implicit biases. The second part of the article applies a very tentative model of mathematical thinking to several cases, or thought experiments, that have been bothering analytical philosophers, practical philosophers, and moral epistemologists. As a result, one can find that the considered thought experiments look very undefined even from a point of view of an imaginary applied mathematician.

This article explores the challenges and limitations of large language models, focusing on ChatGPT as a representative example. We begin by discussing the potential benefits of large language models, such as their ability to generate natural language text and assist with language-related tasks. However, we also acknowledge the concerns around these models, including their environmental impact, potential for bias, and lack of interpretability. We then delve into specific challenges faced by ChatGPT and similar models, including limitations in their understanding of context, difficulty in handling rare or out-of-vocabulary words, and their tendency to generate nonsensical or offensive text. We conclude with recommendations for future research and development, including the need for increased transparency, interpretability, and ethical considerations in the creation and deployment of large language models.