Dataset Information

Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach.

ABSTRACT:

Objectives

This study was conducted to examine gender differences in under-reporting hiring discrimination by building a prediction model for workers who responded "not applicable (NA)" to a question about hiring discrimination despite being eligible to answer.

Methods

Using data from 3,576 wage workers in the seventh wave (2004) of the Korea Labor and Income Panel Study, we trained and tested 9 machine learning algorithms using "yes" or "no" responses regarding the lifetime experience of hiring discrimination. We then applied the best-performing model to estimate the prevalence of experiencing hiring discrimination among those who answered "NA." Under-reporting of hiring discrimination was calculated by comparing the prevalence of hiring discrimination between the "yes" or "no" group and the "NA" group.

Results

Based on the predictions from the random forest model, we found that 58.8% of the "NA" group were predicted to have experienced hiring discrimination, while 19.7% of the "yes" or "no" group reported hiring discrimination. Among the "NA" group, the predicted prevalence of hiring discrimination for men and women was 45.3% and 84.8%, respectively.

Conclusions

This study introduces a methodological strategy for epidemiologic studies to address the under-reporting of discrimination by applying machine learning algorithms.

SUBMITTER: Yoon J

PROVIDER: S-EPMC8920741 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach.

Yoon Jaehong J Kim Ji-Hwan JH Chung Yeonseung Y Park Jinsu J Sorensen Glorian G Kim Seung-Sup SS

Epidemiology and health 20211117

<h4>Objectives</h4>This study was conducted to examine gender differences in under-reporting hiring discrimination by building a prediction model for workers who responded "not applicable (NA)" to a question about hiring discrimination despite being eligible to answer.<h4>Methods</h4>Using data from 3,576 wage workers in the seventh wave (2004) of the Korea Labor and Income Panel Study, we trained and tested 9 machine learning algorithms using "yes" or "no" responses regarding the lifetime exper ...[more]

PMID: 34809416

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Project description:BackgroundThere are more than 300 species of capillariids that parasitize various vertebrate groups worldwide. Species identification is hindered because of the few taxonomically informative structures available, making the task laborious and genus definition controversial. Thus, its taxonomy is one of the most complex among Nematoda. Eggs are the parasitic structures most viewed in coprological analysis in both modern and ancient samples; consequently, their presence is indicative of positive diagnosis for infection. The structure of the egg could play a role in genera or species discrimination. Institutional biological collections are taxonomic repositories of specimens described and strictly identified by systematics specialists.MethodsThe present work aims to characterize eggs of capillariid species deposited in institutional helminth collections and to process the morphological, morphometric and ecological data using machine learning (ML) as a new approach for taxonomic identification. Specimens of 28 species and 8 genera deposited at Coleção Helmintológica do Instituto Oswaldo Cruz (CHIOC, IOC/FIOCRUZ/Brazil) and Collection de Nématodes Zooparasites du Muséum National d'Histoire Naturelle de Paris (MNHN/France) were examined under light microscopy. In the morphological and morphometric analyses (MM), the total length and width of eggs as well as plugs and shell thickness were considered. In addition, eggshell ornamentations and ecological parameters of the geographical location (GL) and host (H) were included.ResultsThe performance of the logistic model tree (LMT) algorithm showed the highest values in all metrics compared with the other algorithms. Algorithm J48 produced the most reliable decision tree for species identification alongside REPTree. The Majority Voting algorithm showed high metric values, but the combined classifiers did not attenuate the errors revealed in each algorithm alone. The statistical evaluation of the dataset indicated a significant difference between trees, with GL + H + MM and MM only with the best scores.ConclusionsThe present research proposed a novel procedure for taxonomic species identification, integrating data from centenary biological collections and the logic of artificial intelligence techniques. This study will support future research on taxonomic identification and diagnosis of both modern and archaeological capillariids.

Dataset Information

Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach.

Objectives

Methods

Results

Conclusions

Publications

Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach.

Similar Datasets

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