Candidate Qualifications Research Articles

Gender Stereotypes and Candidate Qualifications in Judicial Elections

A large literature documents the effects of gendered stereotyping on the outcomes of legislative and executive elections. Less attention has been paid to the effects of these stereotypes on judicial elections, a venue where candidates might encounter unique stereotypes. Further, existing research on judicial elections prioritizes one form of qualification—prior judicial experience—leaving unaddressed the effects of other professional experiences on voters’ choices in these contests. In this paper, we rely on a conjoint experiment to test the effects of candidate qualifications and gender stereotypes in U.S. state judicial elections. We find that, on average, women candidates are advantaged in judicial elections, though we find no evidence that citizens view women candidates as more qualified. Further, while voters do prefer candidates with prior judicial experience they also—contrary to the conventional wisdom—favor judges with prior political experience. Importantly, however, we observe minimal instances of gendered stereotyping in these elections. Our findings call for further research on the effects of gender in judicial elections.

A Rule-Based System for Admission Recommendations in Nigerian Tertiary Institutions

This research presents a rule-based system to provide admission recommendations in Nigerian tertiary institutions. Unlike many existing studies that employ machine learning classifiers, this research aims to develop a transparent and deterministic system using predefined rules. The rule-based system recommends suitable courses based on candidates’ O’level results, Unified Tertiary Matriculation Examination (UTME) scores, computed post-UTME scores, and specific eligibility criteria. By focusing on a rule-based approach, the system mitigates the limitations and potential misclassifications associated with machine learning, thereby enhancing the efficiency and accuracy of the admissions process. The methodology follows predefined guidelines to ensure a transparent and understandable admissions procedure. This approach avoids the complexities and uncertainties of predictive models, providing an accurate and reliable method for course recommendations. The rule-based recommendation system ultimately aims to support educational institutions and admission offices in making informed decisions regarding candidate qualifications, eligibility, and course recommendations.

Campus Recruitment Prediction

For businesses and students alike, campus recruitment is an important occasion. While businesses aim to draw in the best employees, students eagerly anticipate beginning their professional careers. Salary prediction is a crucial component of college recruitment, when employers ascertain the wage ranges, they would offer prospective employees. Many criteria, including the candidate's qualifications, experience, and education, as well as the company's budget and industry norms, play a role in predicting the salary for campus recruitment. In this project, we'll apply machine learning approaches to forecast college recruitment salaries based on candidate historical data and salaries that match to those positions. In this project, we develop a predictive model for college recruitment by analysing the dataset that has been provided. Data processing and exploratory data analysis (EDA) are our initial steps. After that, we build a Flask web application that uses the trained predictive model to be deployed and lets users anticipate things based on input.

SKILL SYNC SELECTOR USING MACHINE LEARNING TECHNIQUES

Our study introduces an Automatic Resume Scanner Application that makes an advantage of advanced natural language processing (NLP). Together with performing thorough linguistic analysis and effectively standardizing resumes, it leverages state-of-the-art natural language processing (NLP) algorithms for sentiment analysis, context interpretation, and profile ranking. Using machine learning algorithms, it matches candidate qualifications with job demands through an intuitive interface that allows criteria to be customized. The application additionally manages bias detection and reduction in order to advance equity and inclusivity in the hiring process. When everything is said and done, it provides a fast, accurate, and neutral way to screen candidates at a preliminary stage, saving time and fostering a diversity of ability. Key Words: Resume screening, machine learning, Natural Language Processing(NLP), Talent acquisition,Text mining, KNeighbors Classifier, OnevsRest Classifier, TF-IDF vectorization, Label encoding, Feature engineering.

Resume Optimisation and Suggestions using Large Language Models: A Review

Abstract: In the rapidly evolving job market, the compatibility of a candidate's resume with the job description is critical to the application process. Conventional resume optimization techniques frequently depend on keyword matching, which is shallow and cannot comprehend linguistic nuances or context, resulting in an inadequate match between applicant's profiles and job criteria. This study presents a novel application that uses Bidirectional Encoder Representations from Transformers (BERT) to generate language embeddings from job descriptions and resumes, therefore bridging this gap. Our program offers a contextaware and nuanced evaluation of how well a candidate's qualifications match the requirements of the position by calculating the similarity scores between these embeddings. Furthermore, the incorporation of Generative Pre-trained Transformer (GPT) models provides customized suggestions for resume optimization, emphasizing areas of improvement indicated by the BERT study. Additionally, the application also has a job search tool that lets the users browse LinkedIn for suitable job posts and apply straight to them.

Developing Racially Ambiguous Job Candidate Avatars with Strong versus Ambiguous Job Candidate Backgrounds in Preparation for a Field Experiment for Enhancing Inclusive Hiring and Reducing Implicit Bias

This study explores the utilization of generative Artificial Intelligence (AI) to create racially ambiguous avatars for job candidate interviews, aiming to enhance inclusive hiring practices and reduce implicit bias. With diversity recognized for boosting organizational performance, addressing hiring discrimination becomes crucial. This research investigates how avatars can anonymize candidates’ racial identities, potentially transforming “blind” hiring to minimize subconscious biases. Through an iterative pilot process engaging 30 female experienced-in-hiring participants with information technology backgrounds, the avatars representing White, Asian, and race-neutral candidate females, and corresponding candidate job descriptions, were refined, setting the foundation for a follow-up field experiment. This experiment will examine avatars’ effects on hiring recommendations and decisions by managers, manipulating strong versus ambiguous candidate qualifications, contributing insights into overcoming implicit biases. The initial pilot findings here suggest promising directions for technology’s role in fostering equitable recruitment, providing a groundwork for future studies on technology-enhanced inclusive hiring strategies.

Perceptions of Electability: Candidate (and Voter) Ideology, Race, and Gender

AbstractPrevious work has shown candidate electability is an important consideration to voters in deciding who to support. However, we do not know what candidate qualities voters consider more electable, especially in the absence of polling information. While scholarship has documented general election penalties for candidates with certain demographic and ideological characteristics, we do not know whether voters actually use these factors when judging electability. Using a conjoint experimental design, we examine how candidate characteristics influence perceptions of candidate electability. We find voters perceive women and minorities as less electable and ideologically extreme candidates as more electable. However, perceptions of electability vary with voter characteristics. Our results indicate that arguments about electability, for many individuals, are based on their own ideological preferences (and to a lesser extent, their identity) rather than systematically viewing candidates with attributes that provide general election advantages as more electable.

Campaign Spending and Its Effect during the 2013 and 2016 Elections in the Province of Cebu

Elections play a crucial role in legitimizing authority in democratic societies, yet the impact of campaign spending necessitates further investigation. This research delves into the influence of campaign expenditures on election outcomes in Cebu province between 2013 and 2016. Analysis of the Statement of Contributions and Expenditures (SOCE), this study explores how candidates make decisions regarding the allocation of campaign funds. Employing a mixed-method research approach, quantitative techniques like regression analysis are utilized to examine the relationship between spending and election results, while qualitative methods including scrutinizing SOCE documents and conducting interviews with candidates and COMELEC officials shed light on spending patterns and offer recommendations for policy enhancements. Cebu Provincial data elections spanning 2013 and 2016 reveal a strong positive correlation between election spending and votes. Travel expenses, compensating campaigners, and communication demonstrate statistically insignificant correlations with voting outcomes (p-values of 0.842, 0.771, and 0.811, respectively). Similarly, printed materials and the employment of watchers’ expenditures display slightly positive correlations but lack statistical significance (p-values of 0.239 and 0.984). Moreover, campaign headquarters, meetings, and rallies show no significant correlations (p-values of 0.841 and 0.458). Advertising expenses emerged as the sole statistically significant factor in 2013 (p = 0.000), indicating a substantial 22.6 increase in votes for every 55,895 pesos spent. In the 2013 elections, only campaign paraphernalia and media spending correlated with votes; conversely, media spending was the sole positive correlation in 2016. Findings suggest that wealth played a significant role in the 2013 election, undermining the democratic principle of meritocracy where the most deserving candidate should prevail. Proposed policy changes include criminalizing campaign malpractices, ensuring candidate qualifications are rigorously vetted, and enhancing voter education programs spearheaded by academia. These measures are essential to mitigate the undue influence of money in politics and uphold the integrity of our electoral system.

Ambiguity in advertised compensation: Recruiting implications of nominal compliance with pay transparency legislation.

Pursuant to legislative mandates the proportion of job postings that include wage and salary information has rapidly increased. However, many organizations comply by advertising very broad salary ranges. Here, we examine how the width of a pay range influences prospective applicants' perceptions. Although in other contexts people often exhibit a preference for vaguely specified gains, we draw from decision and signaling theories to hypothesize negative reactions to highly ambiguous pay ranges and test them in three preregistered experiments. In Study 1, business students evaluate a job posting with the width of the salary range manipulated between subjects. Study 2 tests for moderating effects of ambiguity explanations using a within-subjects manipulation of pay range width counterbalanced across two job postings and a sample of college graduates with relevant work experience. In Study 3, a diverse sample of recent job seekers predict a salary offer for a hiring vignette in which both the extent of ambiguity in the advertised pay range and the chosen candidate's qualifications are manipulated; their qualitative impressions of the organization are also analyzed. Results provide converging evidence for modal aversion to high ambiguity resulting from negative effects on perceived organizational trustworthiness and skewed predictions of salary offers. Practical implications of this aspect of pay transparency are discussed, as well as broader theoretical implications for understanding outcome ambiguity effects in domains where decision makers vary in their beliefs about underlying reasons for vagueness in communicated information. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Potential bias when using social media for selection: Differential effects of candidate demographic characteristics, race match, perceived similarity, and profile detail

AbstractOrganizations are using social media as part of their selection processes. However, little is known about whether bias or discrimination is problematic when using these sources. Therefore, we examined whether manipulating the name and photograph of two otherwise equivalent LinkedIn‐like profiles would influence evaluations of candidate qualifications and hireability as well as perceived similarity using an experimental design. To test our hypotheses based on bias/discrimination research and the similarity‐attraction paradigm, a total of 401 working adults were recruited through Mechanical Turk. No evidence was found for bias or discrimination against women or people of color. However, female candidates were viewed as more hireable than male candidates, and Black men were viewed as less qualified than Black women and White men. Furthermore, we found that perceived similarity increased when the participant's gender or race matched the candidate's gender or race, respectively, and also that perceived similarity was related to candidate ratings; however, neither gender nor race match was directly related to candidate ratings. When profiles were more detailed, participants rated candidates of the same race higher than candidates of other races, and perceived similarity fully indirectly mediated this relationship. Conversely, when less detail was provided, participants rated candidates of the same race lower. Thus, while bias/discrimination toward women and people of color is not inherent when using LinkedIn for selection, having a racially diverse set of selectors is important to ensure fairness. This reveals a nuanced view of diversity issues when using social media for selection.

Smart Resume Analyzer

Smart Resume Analyzer is a project aimed at simplifying the job recruitment process by automating the initial screening of job applications. The system utilizes natural language processing and machine learning techniques to analyze and extract relevant information from a candidate's resume. The extracted information is then used to evaluate the candidate's qualifications against the job requirements, providing a score that reflects their suitability for the position. The system also offers personalized feedback to candidates to help them improve their resumes and increase their chances of landing a job. Smart Resume Analyzer aims to streamline the recruitment process for employers and candidates alike, saving time and increasing the likelihood of finding the best-fit candidates for open positions. The software will be able to extract relevant information such as work experience, education, skills, and achievements, and match them against job descriptions to identify the best fit for the job

Knowing the Competition: Gender, Qualifications, and Willingness to Run in Elections

In spite of significant gains in recent elections, women’s under-representation in U.S. politics remains an empirical fact. Scholars have identified the persistent gender gap in political ambition as a key component. Research to date has focused on explaining the gender gap in political ambition by identifying gender differences in factors such as self-assessed qualifications, recruitment experiences, risk aversion, and competitiveness. The conventional wisdom emerged that because of these gender differences, potential women candidates need more encouragement than men to overcome reservations about running for office. These propositions remain largely untested. Using an innovative experimental design that allows us to directly examine how individuals respond to information about candidate qualifications in real time, we make several substantive contributions to understanding the role of gender in the decision to run for office. First, we find that women are highly responsive to information about their qualifications to run for office, in some cases more so than their male counterparts. In doing so, we shine a spotlight on how men often ignore information about their own unsuitability to run for office. We also find that being presented with multiple chances to run for election has a positive, but limited effect on women’s political ambition.

CODE: Contrastive Pre-training with Adversarial Fine-Tuning for Zero-Shot Expert Linking

Expert finding, a popular service provided by many online websites such as Expertise Finder, LinkedIn, and AMiner, is beneficial to seeking candidate qualifications, consultants, and collaborators. However, its quality is suffered from lack of ample sources of expert information. This paper employs AMiner as the basis with an aim at linking any external experts to the counterparts on AMiner. As it is infeasible to acquire sufficient linkages from arbitrary external sources, we explore the problem of zero-shot expert linking. In this paper, we propose CODE, which first pre-trains an expert linking model by contrastive learning on AMiner such that it can capture the representation and matching patterns of experts without supervised signals, then it is fine-tuned between AMinerand external sources to enhance the model’s transferability in an adversarial manner. For evaluation, we first design two intrinsic tasks, author identification and paper clustering, to validate the representation and matching capability endowed by contrastive learning. Then the final external expert linking performance on two genres of external sources also implies the superiority of adversarial fine-tuning method. Additionally, we show the online deployment of CODE, and continuously improve its online performance via active learning.

Selling them Short? Differences in News Coverage of Female and Male Candidate Qualifications

We draw on research from gender stereotypes and mass communication to develop and test an innovative theoretical framework of implicit and explicit gender framing. This framework delineates how and when coverage in newspapers will report on female candidates differently than male candidates. Implicit gender frames subtly draw on masculine stereotypes to reinforce patriarchal power structures through their coverage of political candidates. Explicit gender frames are the overtly sexist “hair, hemlines, and husband” coverage women receive more frequently relative to men. We argue that the print news media will be more likely to rely on implicit gender frames to elucidate differences between women and men running for political office. Using an exhaustive content analysis of Senate campaign news coverage, we find important differences in the coverage of women and men running against one another. We also find the use of explicit gender frames to be especially common in all-female races. These differences in coverage, especially in all-women contests, can perpetuate stereotypic beliefs that women lack the qualifications needed for political office among voters, and stymie women’s progress toward parity in representation.

The Presidential Primaries: Media Reporting and Candidate Qualities

In the growing field of literature on media reporting and politics, there is a vacancy of knowledge on the study of factors that lead to media reporting. That is, what variables negatively or positively relate to media reporting levels? In this paper, I study that question by looking at data from the presidential primary elections of 2016 and 2020. I look at reporting across over 300 media outlets, for 45 candidates. I analyze eight separate variables and their relationship to media reporting rates. I find strong relationships between reporting rates versus campaign contributions and party identification.

Tennessee Academy of Science Executive Committee Meeting Minutes, November 2021

Tennessee Academy of Science Executive Committee Meeting Minutes, November 2021

An Experiment in Candidate Selection

Are ordinary citizens or political party leaders better positioned to select candidates? While the American primary system lets citizens choose, most democracies rely instead on party officials to appoint or nominate candidates. The consequences of these distinct design choices are unclear: while officials are often better informed about candidate qualifications, they may value traits, like party loyalty or willingness to pay for the nomination, at odds with identifying the best performer. We partnered with both major political parties in Sierra Leone to experimentally vary how much say voters have in selecting Parliamentary candidates. Estimates suggest that more democratic procedures increase the likelihood that parties select voters’ most preferred candidates and favor candidates with stronger records of public goods provision. (JEL D72, H41, O17)

Survey of Dental Anesthesiology program directors' preferences regarding the candidate application and interview process.

Dental Anesthesiology recently gained specialty recognition in 2019. Program directors' preferences for candidates applying to this newly recognized specialty are not well known. This study sought to assess these preferences by surveying the program directors of advanced dental education programs in Dental Anesthesiology. The survey consisted of eleven questions on candidate qualities, academic application criteria, standardized examinations, shadowing, externships, sedation experience, and interview format. The survey achieved a response rate of 100% with all nine program directors completing the survey. The study found that program directors tended to rank integrity, interpersonal/communication skills, assumes responsibility, team player, and reliability among the top five candidate qualities. Program directors tended to rank professional evaluations, institutional evaluations, externship, shadowing (a practicing anesthesiologist), and standardized tests among the top five academic application criteria. Program directors largely preferred differentiating candidates with a standardized examination, such as the Comprehensive Basic Science Examination (CBSE) or the creation of a new examination specific for Dental Anesthesiology application. Program directors largely recommended shadowing and externships but were largely indifferent to a candidate's experience or certification in sedation. All nine program directors opted for virtual interviews in 2020. The findings of this descriptive, cross-sectional study may have significant implications for the candidate application and interview process into the field of Dental Anesthesiology.

Writing an effective and supportive recommendation letter.

Writing recommendation letters on behalf of students and other early-career researchers is an important mentoring task within academia. An effective recommendation letter describes key candidate qualities such as academic achievements, extracurricular activities, outstanding personality traits, participation in and dedication to a particular discipline, and the mentor's confidence in the candidate's abilities. In this Words of Advice, we provide guidance to researchers on composing constructive and supportive recommendation letters, including tips for structuring and providing specific and effective examples, while maintaining a balance in language and avoiding potential biases.

Social identity and coethnic voting in the Middle East: Experimental evidence from Qatar

What explains widespread coethnic voting in the Middle East? The prevailing understanding revolves around clientelism: the view that MENA citizens support coethnic parties and candidates in order to most easily or effectively extract resources from the patrimonial state. Previous research has thus neglected non-economic explanations of ethnic-based preferences and outcomes in MENA elections, including social biases long identified in other settings. This study presents findings from a conjoint survey experiment in Qatar, where symbolic elections lack distributional implications. Consistent with expectations derived from social identity theory, results reveal strong favoritism of cosectarian candidates, whereas objective candidate qualifications do not affect voter preferences. Bias is especially strong in a policy domain – promoting religious values – that prompts respondents to consider the candidate’s ethnic identity. Findings offer clear evidence that ethnic-based voting in Qatar and likely elsewhere is not merely epiphenomenal but can reflect actual preferences for members of social in-groups.