Introduction
Have you ever encountered an “entry-level” job description where candidate requirements include impenetrable aspects like “leverage cross-functional paradigms to optimize synergistic outcomes,” or worse? When HR materials are filled with dense jargon or business terms, they not only confuse readers, but they also scare off talented and capable job seekers. Since the first step to inclusiveness is accessibility, why not ensure your job descriptions maintain an accessible tone through audit processes?
This article shows how to use free and open-source tools like Python and its Text statistics natural language processing (NLP) library to create a script that automates the process of capturing “control language” in job descriptions before publishing them.
The Key Ingredient: The Gunning Fog Index
The Gunning Fog Index – available in Textstat using textstat.gunning_fog – is a great approach for auditing text, especially entry-level job postings. Essentially, this index can be used to estimate the number of years of formal education a person may need to understand a text on first reading.
Its calculation is based on observing two main factors: average sentence length and the percentage of complex terms – generally words with three or more syllables. Note that business jargon typically overuses multi-syllable buzzwords like “operationalization,” “methodologies,” etc. Therefore, the Gunning Fog Index comes close to our intended goal of auditing job descriptions to ensure they are not too complex for the profile they are intended to attract. In other words, it helps ensure that the language is clear and accessible. A lower value for this index means greater clarity and accessibility.
Audit an Example with Textstat
The first crucial step is to install the Textstat library for Python if you haven’t already:
The basic logic of our script will reside in a reusable function whose purpose is to audit entered text — for example, an entry-level job description:
import textstat
def audit_job_description(job_text):
# Calculation of the Gunning Fog index
fog_score = textstat.gunning_fog(job_text)
# Determination of the inclusiveness verdict based on the score
if fog_score < 10:
verdict = "Accessible and inclusive. Ideal for entry level."
elif 10 <= fog_score <= 14:
verdict = "Warning: Approaching the gatekeeper's territory. Simplify some terms."
else:
verdict = "Gatekeeper Alert: High jargon density. Rewrite for clarity."
# Returning a formatted report
return {
"Gunning-Fog Score": fog_score,
"Verdict": verdict
}
The steps followed in the previous function are quite simple. First, we cut to the chase and calculate the Gunning Fog score for the text (presumably a job description) passed as input. This score, stored in fog_score, goes through a simple condition-based check to generate three different verdicts based on the complexity of the text, much like a three-color traffic light system.
Generally speaking, text with a Gunning Fog score below 10 is considered accessible and ideal for an entry-level job description. A score between 10 and 14 is moderately complex, and a score above 14 is considered very complex and requires substantial revision.
Next, it’s time to test our auditor by sending them two different job description examples:
# EXAMPLE 1: A "Gatekeeper" job description
complex_jd = """ The successful candidate will leverage cross-functional paradigms to optimize synergistic deliverables. You will be expected to operationalize key performance indicators and facilitate continuous improvement methodologies to maximize our ROI and institutionalize core competencies across the organizational ecosystem. """
# EXAMPLE 2: An "inclusive" job description
inclusive_jd = """ We are looking for a team player to help us grow our marketing channels. You will work closely with different teams to launch campaigns, track their performance, and find new ways to improve. Your goal is to help us reach more customers and share our brand story. """
print(audit_job_description(inclusive_jd))
To go out:
--- Gatekeeper Job Description ---
{'Gunning-Fog Score': 30.364102564102566,
'Verdict': 'Gatekeeper Alert: High jargon density. Rewrite for clarity.'}
--- Inclusive Job Description ---
{'Gunning-Fog Score': 8.165986394557823,
'Verdict': 'Accessible and inclusive. Ideal for entry level.'}
Our auditor did an excellent job of spotting the first description as a clear “gatekeeper” – a barrier to entry – and recommending that it be rewritten for clarity and inclusiveness. The second description achieved a much lower score of 8.16 (compared to 30.36 for the first, which is comparable to postgraduate research articles in terms of linguistic complexity), confirming that it is well suited to attract entry-level applicants.
Conclusion
Job descriptions are often the gateway to a company, and excessive business jargon can serve as a bouncer in situations where openness matters most, especially for entry-level positions. This article shows how to use Textstat’s Gunning Fog Index to create a simple, automated text listener that identifies overly complex job descriptions, helping to ensure clear, direct, accessible language that keeps your job postings open to all entry-level talent.
Ivan Palomares Carrascosa is a leader, writer, speaker, and advisor in AI, machine learning, deep learning, and LLM. He trains and guides others in leveraging AI in the real world.
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