世界経済・社会統計 = World development indicators

https://link.springer.com/content/pdf/10.2307%2F3343168.pdf

Development as Freedom

Impatti, adattamento e vulnerabilita Le cause e le responsabilita dei cambiamenti climatici sono state trattate sul numero di ottobre della rivista Cda. Approfondiamo l’argomento presentando il documento: “Cambiamenti climatici 2007: impatti, adattamento e vulnerabilita” votato ad aprile 2007 dal secondo gruppo di lavoro del Comitato Intergovernativo sui Cambiamenti Climatici (Intergovernmental Panel on Climate Change). Si tratta del secondo di tre documenti che compongono il quarto rapporto sui cambiamenti climatici.

/pdf/climate-change-2007-impacts-adaptation-and-vulnerability-31xc1cf4d3.pdf

Climate Change 2007: Impacts, Adaptation and Vulnerability.

Watts N, Amann M, Ayeb-Karlsson S, et al. The Lancet Countdown on health and climate change: from 25 years of inaction to a global transformation for public health. Lancet 2018; 391: 581–630—In this Review (published online first on Oct 30, 2017), Jonathan Chambers, Ian Hamilton, Robert Lowe, and Steve Pye's affiliation has been corrected to UCL Energy Institute, London, UK; Fereidoon Owfi and Mahnaz Rabbaniha's affiliation has been corrected to Iranian Fisheries Science Research Institute, AREEO, Tehran, Iran; Meisam Tabatabaei's affiliation has been corrected to Biofuel Research Team, Agricultural Biotechnology Research Institute of Iran, AREEO, Karaj, Iran; and Ali Mohammad Latifi's affiliation has been corrected to Applied Biotechnology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran. In figures 31, 33, and 34, the formatting of the currency in the y-axis title has been corrected to US$2016. In figure 36, the y-axis title has been corrected to “Fossil fuel consumption subsidies (US$2016 billion)”. In figure 37, RGGI has been explained in the figure legend and an asterisk symbol has been removed from the figure. In Table 2, CO2 has been spelled out as carbon dioxide. These corrections have been made to the online version as of Nov 23, 2017, and the printed Review is correct.

The Lancet Countdown on health and climate change: from 25 years of inaction to a global transformation for public health (vol 391, pg 540, 2017)

This paper challenges the validity and usefulness of the regime concept on five separate counts. These lead to two further and secondary (in the sense of indirect), but not less important, grounds for expressing the doubt whether further work of this kind ought to be encouraged. The five counts (or “dragons” to watch out for) are first, that the study of regimes is, for the most part a fad, one of those shifts of fashion not too difficult to explain as a temporary reaction to events in the real world but in itself making little in the way of a long-term contribution to knowledge. Second, it is imprecise and woolly. Third, it is value-biased, as dangerous as loaded dice. Fourth, it distorts by overemphasizing the static and underemphasizing the dynamic element of change in world politics. And fifth, it is narrow-minded, rooted in a state-centric paradigm that limits the vision of a wider reality.

CAVE! HIC DRAGONES: una crítica del análisis de regímenes

Media multitasking, a simultaneous consumption of two or more media, is a ubiquitous and popular behaviour among the youth. One of the reasons for its increasing growth is the structural/market-lev...

Emotions and Media Multitasking Behaviour among Indian College Students

. Mapping of agricultural land use/cover was initiated since the past several decades for land use planning, change detection analysis, crop yield monitoring etc. using earth observation datasets and traditional parametric classifiers. Recently, machine learning, cloud computing, Google Earth Engine (GEE) and open source earth observation datasets widely used for fast, cost-efficient and precise agricultural land use/cover mapping and change detection analysis. Main objective of this study was to assess the transferability of the machine learning algorithms for land use/cover mapping using cloud computing and open source earth observation datasets. In this study, the Landsat TM (L5, L8) of 2018, 2009 and 1998 were selected and median reflectance of spectral bands in Kharif and Rabi season were used for the classification. In addition, three important machine learning algorithms such as Support Vector Machine with Radial Basis Function (SVM-RBF), Random forest (RF) and Classification and Regression Tree (CART) were selected to evaluate the performance in transferability for agricultural land use classification using GEE. Seven land use/cover classes such as built-up, cropland, fallow land, vegetation etc. were selected based on literature review and local land use classification scheme. In this classification, several strategies were employed such as feature extraction, feature selection, parameter tuning, sensitivity analysis on size of training samples, transferability analysis to assess the performance of the selected machine learning algorithms for land use/cover classification. The result shows that SVM-RBF outperforms the RF and CART for both spatial and temporal transferability analysis. This result is very helpful for agriculture and remote sensing scientist to suggest promising guideline to land use planner and policy-makers for efficient land use mapping, change detection analysis, land use planning and natural resource management.

/pdf/assessing-the-transferability-of-machine-learning-algorithms-4ksfn6xica.pdf

Assessing the transferability of machine learning algorithms using cloud computing and earth observation datasets for agricultural land use/cover mapping

Labour law is widely being cited as one of the principal factors creating rigidities in industrial business and, thereby, holding back productivity growth and employment generation. However, a substantial chunk of literature disputes the “rigidity argument” on the ground that there has been a sharp growth in informal employment, which is often considered as flexible labor input as it does not fall under the purview of what is believed to be directly related with rigidity – employment protection legislation (EPL). However, given empirical evidence showing that informal worker is less productive as compared to its formal (regular) counterpart, the question arises: what motivates the employer to employ less productive worker? Using three-dimensional panel data on 28 industrial sectors between 1999–2000 and 2007–2008 across 13 states of India, our results show that the incidence of informal employment is directly linked with EPL, volatility, and labor bargaining power. We utilize instrumental variable two stage least square (IV 2SLS) to overcome the endogeneity issues. The results are robust.

Labour Laws and Informalisation of Employment: Panel Evidences from Indian Formal Manufacturing Sector

A review: The role of geospatial technology in precision agriculture

Favouritism and discrimination reduce labour force diversification while driving unequal growth. The incidence of these practices increases the probability of securing employment for some people ir...

Pritee Sharma

Papers

Emotions and Media Multitasking Behaviour among Indian College Students

Assessing the transferability of machine learning algorithms using cloud computing and earth observation datasets for agricultural land use/cover mapping

Labour Laws and Informalisation of Employment: Panel Evidences from Indian Formal Manufacturing Sector

A review: The role of geospatial technology in precision agriculture

Inter-regional occupational disparities in Indian manufacturing sector with regards to socioeconomic labour attributes