BIAS Word inventory for work and employment diversity, (in)equality and inclusivity (Version 1.0)

The language used in job advertisements contains explicit and implicit cues, which signal employers’ preferences for candidates of certain ascribed characteristics, such as gender and ethnicity/race. To capture such biases in language use, existing word inventories have focused predominantly on gender and are based on general perceptions of the ‘masculine’ or ‘feminine’ orientations of specific words and socio-psychological understandings of ‘agentic’ and ‘communal’ traits. Nevertheless, these approaches are limited to gender and they do not consider the specific contexts in which the language is used. To address these limitations, we have developed the first comprehensive word inventory for work and employment diversity, (in)equality, and inclusivity that builds on a number of conceptual and methodological innovations. The BIAS Word Inventory was developed as part of our work in an international, interdisciplinary project – BIAS: Responsible AI for Labour Market Equality – in Canada and the United Kingdom (UK). Conceptually, we rely on a sociological approach that is attuned to various documented causes and correlates of inequalities related to gender, sexuality, ethnicity/race, immigration and family statuses in the labour market context. Methodologically, we rely on ‘expert’ coding of actual job advertisements in Canada and the UK, as well as iterative cycles of inter-rater verification. Our inventory is particularly suited for studying labour market inequalities, as it reflects the language used to describe job postings, and the inventory takes account of cues at various dimensions, including explicit and implicit cues associated with gender, ethnicity, citizenship and immigration statuses, role specifications, equality, equity and inclusivity policies and pledges, work-family policies, and workplace context.

Classification of Genuinity in Job Posting Using Machine Learning

To avoid fraudulent Job postings on the internet, we target to minimize the number of such frauds through the Machine Learning approach to predict the chances of a job being fake so that the candidate can stay alert and make informed decisions if required. The model will use NLP to analyze the sentiments and pattern in the job posting and TF-IDF vectorizer for feature extraction. In this model, we are going to use Synthetic Minority Oversampling Technique (SMOTE) to balance the data and for classification, we used Random Forest to predict output with high accuracy, even for the large dataset it runs efficiently, and it enhances the accuracy of the model and prevents the overfitting issue. The final model will take in any relevant job posting data and produce a result determining whether the job is real or fake. Keywords: Natural Language Processing (NLP), Term Frequency-Inverse Document Frequency (TF-IDF), Synthetic Minority Oversampling Technique (SMOTE), Random Forest.

OPPORTUNITIES OF JOB SEARCH THROUGH SOCIAL MEDIA PLATFORMS AND ITS DEVELOPMENT IN LITHUANIA

3.81 billion or 49.03 percent of people around the world in 2020 have been using social media platforms. On average, everyone has 8.6 accounts on social media platforms. In today’s world, social media platforms control a large part of life, one of which is job search. Job searches through social media platforms are already completing the elimination of older traditional job search methods, and the social network LinkedIn, which has become an interactive resume, is slowly outpacing resumes and cover letters in terms of the ability to share recommendations and various expertise. Employers are increasingly posting open job positions on social media platforms, making job postings simple and easily accessible to all users of social media platforms. The main goal of the presented paper is to introduce the recommendations for developing the process of job search through social media platforms using quantitative analysis. This article highlights the concept and peculiarities of social media platforms, advantaged and disadvantages of job search through SMP. The factors influencing job search through social media platforms were presented and analyzed according to the survey, steps for the further development were presented as well. Recommendation to improve the process of job search were provided after theoretical, methodological and empirical part. The results of the research will help to define the main advantages and disadvantages of job search through SMP from general population of Lithuania, also main concerns regarding its usage were determined. It is faster and easier to find the job through SMP, although do not like that it is necessary to keep an eye on their profile page in the social media, so privacy concern was defined as the biggest disadvantage. The article used the following methods: scientific literature review, quantative analysis (survey).

Updating the Marketing Research Course to Prepare the Marketing Generalist

This exploratory research suggests that undergraduate marketing research textbooks and courses have not kept pace with the changes in the marketing research world over the past two decades. Two studies, one a review of marketing research syllabi and another a content analysis of online job postings, explore this phenomenon. The results imply that, in contrast to the historical context of marketing research course, most advertised entry-level marketing jobs requiring marketing research skills are not in marketing research firms. Indeed, contemporary marketing research is more likely a function embedded within an array of generalist job duties that also require soft skills and the ability to analyze and present data to upper management. As a result of this research, educators should have a heightened awareness of the following: (a) the disconnect between the marketing research curriculum and current industry needs, (b) the changing role of marketing research as diffused throughout the organization, and (c) the broader set of skills and techniques required of entry-level marketing graduates. As a possible solution to these issues, this research proposes an integrated model whereby instructors can help their students navigate the current landscape by choosing an appropriate pedagogical path to assist students in their career goals.

AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-Scale Examination of Bio-Pharma Firms

Advances in artificial intelligence (AI) could potentially reduce the complexities and costs in drug discovery. We conceptualize an AI innovation capability that gauges a firm’s ability to develop, manage, and utilize AI resources for innovation. Using patents and job postings to measure AI innovation capability, we find that it can affect a firm’s discovery of new drug-target pairs for preclinical studies. The effect is particularly pronounced for developing new drugs whose mechanism of impact on a disease is known and for drugs at the medium level of chemical novelty. However, AI is less helpful in developing drugs when there is no existing therapy. AI is also less helpful for drugs that are either entirely novel or those that are incremental “follow-on” drugs. Examining AI skills, a key component of AI innovation capability, we find that the main effect of AI innovation capability comes from employees possessing the combination of AI skills and domain expertise in drug discovery as opposed to employees possessing AI skills only. Having the combination is key because developing and improving AI tools is an iterative process requiring synthesizing inputs from both AI and domain experts during both the development and the operational stages of the tool. Taken together, our study sheds light on both the advantages and the limitations of using AI in drug discovery and how to effectively manage AI resources for drug development.