# Getting Started
Have you ever stumbled upon a “entry-level role” posting filled with bewildering demands like “leveraging cross-functional paradigms for optimizing synergistic outcomes,” or phrases even more convoluted? When HR materials are packed with heavy jargon or niche corporate buzzwords, they don’t just baffle readers—they also unintentionally discourage skilled and enthusiastic applicants. Given that the first move toward fairness is accessibility, why not audit your job postings to make sure the tone is welcoming and readable?
This guide demonstrates how to leverage free, open-source Python tools along with the Textstat natural language processing (NLP) library to create a script that automatically detects “gatekeeping language” in job listings before they go live.
# The Key Metric: Gunning Fog Index
The Gunning Fog Index — accessible in Textstat through textstat.gunning_fog — is a fantastic method for reviewing text, especially for entry-level position postings. At its core, this index estimates the number of years of schooling someone might need to grasp the content on an initial reading.
It is determined by looking at two primary metrics: average sentence length and the proportion of complex words — generally those with three or more syllables. Corporate jargon frequently overuses lengthy buzzwords like “operationalization” or “methodologies.” Consequently, the Gunning Fog Index aligns well with our goal of auditing job postings to confirm they aren’t overly intricate for the audience we wish to reach. Put simply, it confirms the writing is straightforward and easy to follow. A smaller score implies better clarity and ease of access.
# Reviewing an Example Using Textstat
The primary initial step is to set up the Python Textstat library if you haven’t already:
The primary framework of our script is housed within a flexible function designed to audit any provided text — for instance, an entry-level position description:
import textstat
def audit_job_description(job_text):
# Determining the Gunning Fog Index
fog_score = textstat.gunning_fog(job_text)
# Deciding the inclusivity rating based on the result
if fog_score < 10:
verdict = "Accessible & Inclusive. Perfect for entry-level positions."
elif 10 <= fog_score <= 14:
verdict = "Caution: Reaching gatekeeper threshold. Try simplifying certain language."
else:
verdict = "Gatekeeper Alert: Dense jargon detected. Revise for better clarity."
# Providing a structured report
return {
"Gunning-Fog Score": fog_score,
"Verdict": verdict
}The actions performed in that function are very direct. We start by computing the Gunning Fog rating for the text (a job listing, for example) given as input. This result, kept in fog_score, undergoes a series of basic checks to create three distinct verdicts based on how complex the language is — functioning much like a traffic light system.
In general, a text receiving a Gunning Fog score under 10 is treated as readable and perfect for an entry-level role. A score ranging from 10 to 14 shows moderate complexity, whereas any score over 14 is seen as extremely complicated and calls for significant rephrasing.
Moving forward, let’s try out our auditing tool using two sample job descriptions:
# EXAMPLE 1: A "Gatekeeper" Style Description
complex_jd = """
The ideal candidate will leverage cross-functional paradigms to optimize synergistic outputs.
You’ll be required to operationalize key performance indicators and enable ongoing improvement methods
to maximize our return on investment and embed core strengths across the wider organization.
"""
# EXAMPLE 2: An "Inclusive" Style Description
inclusive_jd = """
We’re on the lookout for a collaborative team member to support our marketing efforts.
You’ll partner with various departments to run campaigns, monitor results, and discover ways to enhance them.
Your mission is to help us connect with more followers and communicate our story.
"""
print("--- Gatekeeper Job Description ---")
print(audit_job_description(complex_jd))
print("n--- Inclusive Job Description ---")
print(audit_job_description(inclusive_jd))Result:
--- Gatekeeper Job Description ---
{'Gunning-Fog Score': 30.364102564102566, 'Verdict': 'Gatekeeper Alert: Dense jargon detected. Revise for better clarity.'}
--- Inclusive Job Description ---
{'Gunning-Fog Score': 8.165986394557823, 'Verdict': 'Accessible & Inclusive. Excellent for entry-level roles.'}Our tool worked well in identifying the first description as a definite “gatekeeper” — creating an unnecessary obstacle — and suggesting it be rewritten for ease and inclusivity. The second version earned a far lower mark of 8.16 (versus the initial 30.36, a score resembling advanced academic research in difficulty), verifying it’s a great fit for drawing in entry-level applicants.
# Final Thoughts
Job postings often serve as the initial impression of a company, and too much corporate lingo can function like a barrier exactly when openness is key — particularly for junior roles. This overview illustrated how to employ Textstat’s Gunning Fog Index to build an automated tool that flags overly complicated job listings, supporting clear, simple, and welcoming language to ensure your vacancies appeal to all qualified newcomers.
Iván Palomares Carrascosa is a leader, writer, speaker, and consultant in AI, machine learning, deep learning & LLMs. He provides training and mentorship on implementing AI solutions effectively.
