A Comprehensive Review of the Three Main Topic Modeling Algorithms and Challenges in Albanian Employability Skills

Today’s jobseekers face many obstacles while trying to find a career that aligns with their interests, employability soft skills, and professional experience. In Albania, jobseekers frequently initiate their job search by actively exploring job vacancies listed on various online job portals. The analysis of job vacancies posted online provides an added advantage to the labour market actors compared to traditional survey-based analyses. This is because it enables a faster analytical process, promotes decision-making based on accurate data, and should be carefully considered by every country when formulating their Labor Market Policies. Since the data posted online are unlabelled, it has been proven that the potential of unsupervised learning techniques, more precisely the Topic Modelling algorithms, is outstanding when applied to analysing job vacancies, mainly with regard to assessing employability soft skills. Algorithms in topic modelling are essential for uncovering hidden patterns in texts, facilitating the extraction of important data, generating document summaries, and enhancing content comprehension. This paper analyses and compares the three primary methodologies and algorithms used in topic modelling, which can be applied to analyse employability soft-skills: Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and BERTopic. At the end of the paper, conclusions are drawn regarding superior performance and optimal algorithm applicability, challenges, and limitations through a review of studies conducted in the Albanian job market

DataOps for Societal Intelligence: a Data Pipeline for Labor Market Skills Extraction and Matching

Big Data analytics supported by AI algorithms can support skills localization and retrieval in the context of a labor market intelligence problem. We formulate and solve this problem through specific DataOps models, blending data sources from administrative and technical partners in several countries into cooperation, creating shared knowledge to support policy and decision-making. We then focus on the critical task of skills extraction from resumes and vacancies featuring state-of-the-art machine learning models. We showcase preliminary results with applied machine learning on real data from the employment agencies of the Netherlands and the Flemish region in Belgium. The final goal is to match these skills to standard ontologies of skills, jobs and occupations

Determining systematic differences in human graders for machine learning-based automated hiring

Firms routinely utilize natural language processing combined with other machine learning (ML) tools to assess prospective employees through automated resume classification based on pre-codified skill databases. The rush to automation can however backfire by encoding unintentional bias against groups of candidates. We run two experiments with human evaluators from two different countries to determine how cultural differences may affect hiring decisions. We use hiring materials provided by an international skill testing firm which runs hiring assessments for Fortune 500 companies. The company conducts a video-based interview assessment using machine learning, which grades job applicants automatically based on verbal and visual cues. Our study has three objectives: to compare the automatic assessments of the video interviews to assessments of the same interviews by human graders in order to assess how they differ; to examine which characteristics of human graders may lead to systematic differences in their assessments; and to propose a method to correct human evaluations using automation. We find that systematic differences can exist across human graders and that some of these differences can be accounted for by an ML tool if measured at the time of training

Inhibiting and stimulating factors for the integration of refugees into the Portuguese labour market - a psychological perspective

Employment is considered a fundamental pillar of the overall refugee integration. From a psychological perspective, there are several factors thataffect the refugees’ labour market outcome. Following a dual approach,in-depth interviews with 2 psychologists and 13 Middle Eastern and East African refugees in Portugal were conducted. The analysis of the results indicates 8 key emotions, which dominate the refugees’ psychological reality. Together with identified structural circumstances, these emotions affect refugees' mental health and thereby either stimulate or inhibit their integration into the society and labour market. The obtained findings have important implications for employers such as the Portuguese social business Mezze

Analyzing Business-Focused Social Networks in Hiring: The Influence of a Job Candidate\u27s Network on a Recruiter\u27s Hiring Recommendation

Social media has altered the ways in which people interact. Business-focused social media profiles, such as those on LinkedIn, can act as a proxy for a traditional resume. However, these websites differ from a traditional resume in that information presented is sometimes informal, personal, and irrelevant to the member’s career. Furthermore, HR employees are able to view a job candidate’s social network. This research investigates the influence of a recruiter’s knowledge of an applicant’s professional network on the recruiter’s perception of the applicant’s trustworthiness and hence their willingness to take risk in the hiring relationship. A review of the literature covered two areas of research: trust and the use of social networks in hiring. While previous studies connected the trust model to LinkedIn, none of them addressed the influence of a LinkedIn profile’s social network on a hiring manager’s perception of the candidate’s trustworthiness. A survey-based experiment was designed to evaluate how network association bias, a newly created construct, affects a hiring manager’s perception of a job candidate’s ability and benevolence. The experimental model was based on Mayer, Davis, and Schoorman’s trust model. A structural equation modeling (SEM) analysis was conducted in RStudio using the lavaan latent variable modeling package. ix The results of this experiment reveal that that a job candidate’s social network impacts how the candidate’s levels of ability and benevolence are perceived by others. Furthermore, it is suggested that a recruiter’s propensity to trust influences the relationship between network association bias and a job candidate’s ability

Influence of Leadership Approaches on Intrinsic Motivation of Career Professionals in Ontario Non-profit Employment Agencies: An Exploratory Case Study

This exploratory case study (Yin, 2009) investigated those leadership approaches that are used by mid-level managers of seven non-profit employment agencies in Ontario, Canada, to support the intrinsic motivation of seven career professionals who work with them. Unlike profit-earning organizations, career professionals of non-profit employment agencies in Ontario do not get any additional financial incentives for exceeding their targets of helping job seekers find sustainable employment. The research used a transformative learning theory lens (Mezirow, 1991), and also an Interpretivist framework (Merriam, 1998) to understand the data. This study also sought to find what motivates career professionals to reach and exceed their pre-set targets without the availability of any additional bonus. Seven mid-level managers and seven career professionals of non-profit employment agencies were interviewed. A semi-structured interview format was used for the one-on-one interviews. Additional data were collected via document perusal, and the researcher’s reflective journals. Data were coded and analyzed thematically using a content analysis method. Triangulation and member-checking were performed for ensuring reliability of data (Yin, 2009). Findings of the study suggest that mid-level managers of Ontario non-profit employment agencies largely use transformational leadership approaches for building and sustaining career professionals’ intrinsic motivation, although, they sometimes use transactional approaches as well. The study also suggests that the career professionals of the seven non-profit employment agencies are by and large, intrinsically motivated, and three of their key motivators are “passion for their jobs”, “empathy for the clients” and “changing other people’s lives”

The Governance of Migration Policy

In this paper, I examine high-income country motives for restricting immigration. Abundant evidence suggests that allowing labor to move from low-income to high-income countries would yield substantial gains in global income. Yet, most high-income countries impose strict limits on labor inflows and set their admission policies unilaterally. A core principle underlying the World Trade Organization is reciprocity in tariff setting. When it comes to migration from poor to rich countries, however, labor flows are rarely bidirectional, making reciprocity moot and leaving labor importers with all the bargaining power. One motivation for barriers to labor inflows is political pressure from groups that are hurt by immigration. Raising immigration would depend on creating mechanisms to transfer income from those that immigration helps to those that it hurts. Another motivation for immigration restrictions is that labor inflows from abroad may exacerbate distortions in an economy associated with redistributive tax and transfer policies. Making immigration more attractive would require creating mechanisms that limit the negative fiscal impacts of labor inflows on natives. Fiscal distortions create an incentive for receiving countries to screen immigrants according to their perceived economic impact. For high skilled immigrants, screening can be based on educational degrees and professional credentials, which are relatively easy to observe. For low skilled immigrants, illegal immigration represents an imperfect but increasingly common screening device. For policy makers in labor-importing nations, the modest benefits freer immigration brings may simply not be worth the political hassle. To induce high-income countries to lower border barriers, they need to get more out of the bargain.international migration, labor mobility, political economy, illegal migration

AIRM: a new AI Recruiting Model for the Saudi Arabian labour market

One of the goals of Saudi Vision 2030 is to keep the unemployment rate at the lowest level to empower the economy. Prior research has shown that an increase in unemployment has a negative effect on a country’s Gross Domestic Product. This research aims to utilise cutting-edge technology such as Data Lake (DL), Machine Learning (ML) and Artificial Intelligence (AI) to assist the Saudi labour market bymatching job seekers with vacant positions. Currently, human experts carry out this process; however, this is time consuming and labour intensive. Moreover, in the Saudi labour market, this process does not use a cohesive data centre to monitor, integrate, or analyse labour market data, resulting in inefficiencies, such as bias and latency. These inefficiencies arise from a lack of technologies and, more importantly, from having an open labour market without a national labour market data centre. This research proposes a new AI Recruiting Model (AIRM) architecture that exploits DLs, ML and AI to rapidly and efficiently match job seekers to vacant positions in the Saudi labour market. A Minimum Viable Product (MVP) is employed to test the proposed AIRM architecture using a labour market dataset simulation corpus for training purposes; the architecture is further evaluated against three research-collaborative Human Resources (HR) professionals. As this research is data-driven in nature, it requires collaboration from domain experts. The first layer of the AIRM architecture uses balanced iterative reducing and clustering using hierarchies (BIRCH) as a clustering algorithm for the initial screening layer. The mapping layer uses sentence transformers with a robustly optimised BERTt pre-training approach (RoBERTa) as the base model, and ranking is carried out using the Facebook AI Similarity Search (FAISS). Finally, the preferences layer takes the user’s preferences as a list and sorts the results using the pre-trained cross-encoders model, considering the weight of the more important words. This new AIRM has yielded favourable outcomes: This research considered accepting an AIRM selection ratified by at least one HR expert to account for the subjective character of the selection process when exclusively handled by human HR experts. The research evaluated the AIRM using two metrics: accuracy and time. The AIRM had an overall matching accuracy of 84%, with at least one expert agreeing with the system’s output. Furthermore, it completed the task in 2.4 minutes, whereas human experts took more than six days on average. Overall, the AIRM outperforms humans in task execution, making it useful in pre-selecting a group of applicants and positions. The AIRM is not limited to government services. It can also help any commercial business that uses Big Data