source: kdnuggets: the ‘entry-level’ gatekeeper: auditing job descriptions with textstat
level: technical
job descriptions for entry-level roles often contain dense jargon that confuses readers and deters qualified candidates. the gunning fog index, available in the textstat library, estimates the years of education needed to understand a text on first read. it considers average sentence length and the percentage of complex words, typically those with three or more syllables. business jargon frequently uses such multi-syllable terms, making this index a practical tool for auditing job listings.
a simple python function can calculate the gunning fog score for a given job description and assign a verdict. scores below 10 indicate accessible, inclusive language ideal for entry-level roles. scores between 10 and 14 suggest caution and recommend simplifying some terms. scores above 14 flag the text as a gatekeeper alert, requiring substantial revision to improve clarity. this automated check acts like a traffic light system for language complexity.
testing the auditor on two sample descriptions showed clear results. a jargon-heavy description scored 30.36, comparable to postgraduate research papers, and was flagged for rewriting. a straightforward, inclusive description scored 8.16, confirming its suitability for attracting entry-level talent. integrating such a script into the job posting workflow can help companies ensure their listings remain open and welcoming to all potential applicants.
why it matters: automated readability checks help data science and hr teams reduce bias in hiring by ensuring job descriptions are clear and accessible, widening the candidate pool.
source: kdnuggets: the ‘entry-level’ gatekeeper: auditing job descriptions with textstat