Digital Literacy: Results of a Public Policy Analysis Illustrated (By an Example of Asian Countries)

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Аннотация

Digital literacy under conditions of global transformation of the economy and society has become the most important factor for socio-economic growth. The development of technology has fundamentally changed the speed of information exchange, which has led to profound changes in social relations. The labor market conditions have undergone qualitative changes. Digital skills and competencies have come to the fore. In this context, digital literacy becomes a key factor in personal development. Currently, the research into the results of government measures aimed at increasing digital literacy, both in the field of law and in the socio-economic sphere, increased in relevance. This article is about the methods for analyzing the fundamental results of public policy. The regulatory documents of India (Right to Digital Literacy Bill, December 2022), Indonesia (National Digital Literacy Program, 2021) and Singapore (Digital Readiness Blueprint, 2018). The study results allow to identify the role of government measures in the field of law, the leading factors in the population digital literacy evolution processes, as well as the country-specific characteristics of these processes.

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Авторлар туралы

Anna Yakovleva

The Herzen State Pedagogical University of Russia

Хат алмасуға жауапты Автор.
Email: yeif@mail.ru
ORCID iD: 0000-0001-9219-9405
SPIN-код: 4603-0174

Dr. Sci. (Econ.), Professor, Associate Professor

Ресей, Saint Petersburg

Pavel Konyukhovskiy

The Herzen State Pedagogical University of Russia

Email: kon_pv@mail.ru
ORCID iD: 0000-0002-2940-1049
SPIN-код: 9637-6933

Dr. Sci. (Econ.), Professor

Ресей, Saint Petersburg

Әдебиет тізімі

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Әрекет
1. JATS XML
2. Figure 1. A block diagram for calculating the Digital Skills Gap Index (DSGI).

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3. Fig. 2. DSGI rating by country, 2021

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4. Fig. 3. DSGI rating by country, 2021

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5. Figure 4. DSGI rating by country, 2021

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6. Fig. 5. The correlation diagram of the characteristics of socio-economic development in the group of countries under consideration.

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7. Fig. 6. Heat map of the characteristics of socio-economic development in the group of countries under consideration.

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8. Fig. 7. The "broad" regression model is the index of digital skills gap from all variables.

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9. Figure 8. The "narrow" regression model is the digital skills gap index (DigitGapInd) from the share of the urban population (ShareCityPopul).

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10. Fig. 9. Examples of alternative "narrow" regression models—digital skills gap index (DigitGapInd) from various variables (RsrchDevExp, LiteracyRate, GDP_per_capita).

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11. Figure 10. Distribution of variance when applying the principal component method to a dataset from Table 2.

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12. Figure 11. Regression model—digital skills gap index (DigitGapInd) from the main components pc1, pc2, pc3.

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13. Figure 12. Regression model—digital skills gap index (DigitGapInd) from the main component pc1.

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14. 13. Indicators and observations (Table 2) in the coordinate system of the main components pc1 and pc2.

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