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- W4313133770 abstract "Indian residents now have an opportunity to obtain information that is kept in government offices thanks to the Right to Information (RTI) Act of 2005. The RTI query-log data, which records citizen interactions with the government, is available in every government office. The goal of this work is to analyses the RTI query-log data in order to find fundamental latent patterns, with the hope that these patterns may provide insight into prospective legislative changes. We gather RTI queries and reply-statistics from government educational institutions all throughout India for the first time. Three latent patterns are quantified: I the organizations' transparency as determined by RTI queries stats; (ii) the influences on organizations’ accessibility; and (iii) the success of the RTI law's execution. There is a suggestion for this double institute-query-category matrix representation of the RTI data. By employing an Evaluated Response Model to estimate the data matrix's maximum probability, the aforementioned three latent parameters are simultaneously measured. We find contradictions in the RTI legislation's application through the latent patterns that were identified, while also providing recommendations for possible RTI act reform." @default.
- W4313133770 created "2023-01-06" @default.
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- W4313133770 date "2022-10-03" @default.
- W4313133770 modified "2023-09-27" @default.
- W4313133770 title "Indian RTI Act-2005 Analysis by Machine Learning: A Bastar Zone Perspective" @default.
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- W4313133770 doi "https://doi.org/10.1109/icccnt54827.2022.9984586" @default.
- W4313133770 hasPublicationYear "2022" @default.
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