The biggest barrier to policing social media is language. Based on a draft LIRNEasia white paper on neural language processing. Published in Foreign Policy. 
The biggest barrier to policing social media is language. Based on a draft LIRNEasia white paper on neural language processing. Published in Foreign Policy.
Prof. Rohan Samarajiva Kathmandu Marriott Hotel, Nepal May 02 2019 Workshop on ‘Enabling the disabled: ICT access and use by the persons with disabilities in Nepal’
LIRNEasia proposed simple, immediately actionable ways to promote independent living by persons with disabilities (PWDs) in Nepal.
Recommendations to support independent living for persons with disabilities Brief in Nepali and English
Recommendations to support independent living for persons with disabilities Brief in Nepali and English
Recommendations to support independent living for persons with disabilities Brief in Nepali and English
Recommendations to support independent living for persons with disabilities Brief in Nepali and English
Recommendations to support independent living for persons with disabilities Brief in English and Nepali
Recommendations to support independent living for persons with disabilities Brief in English and Nepali
Recommendations to support independent living for persons with disabilities
Recommendations to support independent living for persons with disabilities Brief in English and Nepali
A whitepaper distilling LIRNEasia's current thoughts on the possibilities and issues with the computation extraction of syntactic and semantic language from digital text.
Uncertain of the answer, I thought I’d write about it. Since Cable published their latest pricing league tables earlier this year my inbox seems to have a magnetic effect attracting ‘news’ from joyous announcements by the providers of the cheapest data in the world to harsh commentaries on the inaccuracy of the data being published. Following Cable’s release was A4Ai with its league tables published in March 2019 with prices for 100 MB, 200 MB, 1 GB, 2GB, 5GB and 10GB as a percentage of average income (GNI per capita reported by the World Bank). Both pricing leagues are relatively recent albeit more nuanced in some ways in comparison to the more seasoned data annually released by the OECD (with the limitation that this is only for the OECD countries) and the International Telecommunication Union (ITU). Do we need this many pricing league tables for what appears to be the same thing?
The regional gender findings from AfterAccess were recently featured in the United Nations University-Equals’ Global Partnership’s inaugural report, Taking Stock: Data and Evidence on Gender Digital Equality in Digital Access, Skills and Leadership. The report has been combined in an effort to provide a resource for decision makers who are interested in reducing gender disparities in ICT access and use. LIRNEasia contributed to a chapter, Towards understanding the digital gender gap in the Global South, based largely on the nationally representative AfterAccess survey data from 17 (of the 23 surveyed) countries. The chapter relates some of the challenges in collecting rigorous gender-disaggregated data, and then illustrates the magnitudes of the gaps in access to mobile phones, internet and social media in the three regions. The chapter also examines gender digital inequality in the three regions through different lenses and methods.
Method. Method. Method. But it seems that rankings and the publicity that ensues takes precedence. Any methodology, for it to be meaningful, needs to be transparent and to the extent possible ensure comparability.