The ‘Entry-Level’ Gatekeeper: Auditing Job Descriptions with Textstat
The article discusses the importance of clear and accessible language in job descriptions, particularly for entry-level positions. It introduces the Gunning Fog Index as a tool to evaluate the complexity of job listings and ensure they are inclusive. By using the Textstat library in Python, employers can automate the auditing process to avoid jargon that may deter potential candidates.
- ▪The Gunning Fog Index estimates the years of education needed to understand a text based on sentence length and complex terms.
- ▪Job descriptions with a Gunning Fog score below 10 are considered accessible and ideal for entry-level positions.
- ▪The article provides a Python script using Textstat to audit job descriptions for clarity and inclusivity.
Opening excerpt (first ~120 words) tap to expand
# Introduction Have you ever come across an "entry-level" job description in which candidates' requirements include impenetrable aspects like "leveraging cross-functional paradigms for optimizing synergistic outcomes", or even worse? When HR documents are full of dense jargon or business terms, they not only confuse readers but also scare talented, capable job seekers away. Since the first step towards inclusivity is accessibility, why not ensure your job descriptions keep an accessible tone through auditing processes? This article shows how to use free, open-source tools like Python and its Textstat natural language processing (NLP) library to build a script that automates the process of capturing "gatekeeping language" in job descriptions before publishing them.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at KDnuggets.
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