Educators’ interpretations of children’s belonging across borders: Thinking and talking with an image

European quality framework for early childhood education and care calls for creating environments that support all children’s sense of belonging. This study aims to advance empirical knowledge on educators’ interpretations of children’s belonging in early education settings. The study is part of a project conducted in five European countries – Finland, Iceland, the Netherlands, Norway and Sweden. The following research question guides the study: How do educators interpret children’s belonging in early education across borders? The study draws from the theory of the politics of belonging by Yuval-Davis and employs ‘thinking and talking with an image’ as a methodological approach. The findings explicate educators’ taken-forgranted categorisations, thus portraying their views about educational settings as sites for children’s belonging. Opposing, joint play and being alone were identified as emotionally loaded interactions that educators interpreted as significant for children’s belonging. The educators emphasised democratic values, such as diversity, participation, equality and equity. However, they viewed diverse tensions in embodying democratic values in a diverse group. The shared basis of the profession appeared as a more significant basis for educators’ interpretations than the different societal contexts. The study encourages educators and researchers in European countries to collaborate in promoting children’s belonging.

A quality framework for statistical algorithms

As national statistical offices (NSOs) modernize, interest in integrating machine learning (ML) into official statisticians’ toolbox is growing. Two challenges to such an integration are the potential loss of transparency from using “black-boxes” and the need to develop a quality framework. In 2019, the High-Level Group for the Modernisation of Official Statistics (HLG-MOS) launched a project on machine learning with one of the objectives being to address these two challenges. One of the outputs of the HLG-MOS project is a Quality Framework for Statistical Algorithms (QF4SA). While many quality frameworks exist, they have been conceived with traditional methods in mind, and they tend to target statistical outputs. Currently, machine learning methods are being looked at for use in processes producing intermediate outputs, which lead to a final statistical output. Therefore, the QF4SA does not replace existing quality frameworks; it complements them. As the QF4SA targets intermediate outputs and not necessarily the final statistical output, it should be used in conjunction with existing quality frameworks to ensure that high-quality outputs are produced. This paper presents the QF4SA, as well as some recommendations for NSOs considering the use of machine learning in the production of official statistics.

A framework analysis approach of an indoor air quality sensor

Killing around 7 million people a year, air pollution is the biggest risk to environmental health in the world. In this paper, we explore the use of structuring knowledge representation in form of a framework approach for an indoor air quality sensor. Applying the main steps to be considered in defining an air quality framework, we discuss each one of them, followed by a particular implementation of the framework in terms of an ontology model to exposure to carbon monoxide and PM10.5 (two of the most encountered pollutants in home life).

Product and Process Analysis of Machine Translation into the Inflectional Language

This study focuses on the influence of quality of Machine Translation (MT) output on a translator’s performance. We analyze the translator’s effort by product analysis and process analysis. The product analysis consists of MT quality evaluation according to the Dynamic Quality Framework; using error typology and the criteria such as fluency and adequacy. We examine translator’s effort from the point of view of typing time, in the context of MT quality—focusing on error rate in language, accuracy, terminology, and style, and also in fluency and adequacy to the source text. We have found that the translator’s performance is influenced by MT quality. The typing time is very closely related to errors in language, accuracy, terminology, and style as well as to fluency and adequacy. We used the Mann-Whitney test to compare the productivity of post-editing of MT with human translation. The results of the study have shown that post-editing—compared to human translation of journalistic text from English into the inflectional Slovak language is more effective.

Quality Management In Construction Industry By Using Building Information Modelling (BIM)

Building Information Modeling has the potential to help the construction sector change its design & construction processes. While BIM is thought to assist improve design designed to remove disputes and minimising rebuilding, little study has been done on its application in projects for construct quality control & data management. The promise of BIM implementation in quality management rests in its capacity to offer multi-dimensional data, combining design data and time sequences, due to the compatibility of project specifications using quality control processes and quality control processes. The advantages using 6D BIM regarding quality framework depends upon on architectural code are examined and discussed in this study.