English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The aim of this study is to classify different types of occupations according to similarities in their requirements with regard to language skills. To do so, we use data on these occupations from the O NET-SOC-2010 for the USA in 2015. Three classifications, depending on the level of disaggregation of the O’NET data, are considered. Our analysis is conducted using artificial neural networks – specifically, Kohonen’s self-organizing maps (SOM) – to determine whether the level of disaggregation of the occupations influences their classification by the level of linguistic competence required. This is a novel contribution since, as far as we know, no previous study has used SOMs to classify occupations according to required language competence. Our analysis may help social researchers to study how the linguistic components of an occupation influence productivity, worker employability, business results, and the generation of competitive advantages based on language, etc.

Las ocupaciones laborales en función de los requisitos de competencias lingüísticas: una categorización mediante redes neuronales artificiales