The reason why the media is sometimes referred to as the fourth branch of government is because the media can influence the decisions of the government and the public.
The media in every society can be very influential, they release information which can positively or negatively impact the actions of the governing body or even the general public.
By referring the media as the fourth branch of government, they check or monitor the various actions of the political leaders and this can influence how the general public view their government. The media is powerful and can positively or negatively influence the public.
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Explanation:
Answer:
core nations exploit peripheral nations
Explanation:
Immanuel Wallerstein is one of the dependency theorists that explains global inequality in the international system, According to his neo marxist theory:
Global stratification occurs as core nations exploit the peripheral nations that basically are producers of raw materials and generate cheap labor force.
The dependency theory suggests that the current capitalist system perpetuates inequalities since it reproduces conditions where global markets are controlled by nations like England or the US that are at the core, while other states are semi peripheral and serve as a bridge.
In other words: Dependency theory states that as long as peripheral nations keep relying on core nations for economic stimulus and access to a larger piece of the global economy, they will never gain the stable and consistent economic growth promised.
dzmitry bahdanau, kyunghyun cho, and yoshua bengio. 2014. neural machine translation by jointly learning to align and
Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance.
The models proposed as of late for brain machine interpretation frequently have a place with a group of encoder-decoders and comprises of an encoder that encodes a source sentence into a fixed-length vector from which a decoder creates an interpretation.
In this paper, we guess that the utilization of a fixed-length vector is a bottleneck in working on the exhibition of this essential encoder-decoder engineering, and propose to broaden this by permitting a model to naturally (delicate )look for parts of a source sentence that are pertinent to anticipating an objective word, without shaping these parts as a hard section unequivocally.
With this new methodology, we accomplish an interpretation execution equivalent to the current cutting edge state put together framework with respect to the undertaking of English-to-French interpretation. Moreover, subjective examination uncovers that the (delicate )arrangements found by the model concur well with our instinct.
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