‘Entry-level’ gatekeepers: auditing job descriptions with textstat

# Introduction

Have you ever seen an “entry-level” job description in which the candidate’s requirements included impenetrable aspects like “leverage cross-functional paradigms to optimize synergistic outcomes” or worse? When HR documents are filled with dense jargon or business jargon, they not only confuse readers, but also scare away talented, capable job seekers. Since the first step toward inclusivity is accessibility, why not ensure that your job descriptions maintain an accessible tone through auditing processes?

This article shows how to use Python and free, open-source tools like it. textstate Natural language processing (NLP) library for creating a script that automates the process of capturing “gatekeeping language” in job descriptions before they are published.

# Main components: Gunning Fog Index

gunning fog index – Available by using in TextState textstat.gunning_fog – Audit text is an excellent approach, especially for entry-level job listings. In short, this index can be used to estimate how many years of formal education a person may need to understand a text the first time they read it.

Its calculation is based on looking at two main factors: average sentence length and the percentage of complex words – usually words with three syllables or more. Note that business jargon commonly misuses multi-syllable keywords such as “operational”, “functionality”, etc. Therefore, the Gunning Fog Index gets closer to our desired goal of auditing job descriptions to ensure that they are not overly complex for the desired profile they are meant 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.

# Auditing an example with TextStat

The first important step is to install the textstat library for Python if you haven’t done so yet:

The main logic of our script will reside in a reusable function whose purpose is to audit an input text – for example an entry level job description:

import textstat

def audit_job_description(job_text):
    # Calculating the Gunning Fog Index
    fog_score = textstat.gunning_fog(job_text)

    # Determining the inclusivity verdict based on the score
    if fog_score < 10:
        verdict = "Accessible & Inclusive. Ideal for entry-level."
    elif 10 <= fog_score <= 14:
        verdict = "Caution: Approaching gatekeeper 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 taken in the previous function are quite simple. First, we go straight to the point and calculate the Gunning Fog score for the text passed as input (possibly a job description). This score, stored in fog_scoreIt goes through a simple condition-based check to generate three different decisions depending 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 entry-level job descriptions. A score between 10 and 14 is slightly complex, and a score above 14 is considered highly complex and requires substantial modification.

Next, it’s time to test our auditor by giving him two different examples of job descriptions:

# 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 return on investment 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 how well they do, and find new ways to improve.
Your goal is to help us reach more customers and share our brand story.
"""

print("--- Gatekeeper Job Description ---")
print(audit_job_description(complex_jd))

print("n--- Inclusive Job Description ---")
print(audit_job_description(inclusive_jd))

Output:

--- 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 & Inclusive. Great for entry-level.'}

Our auditor did a great job of identifying the first description as an obvious “gatekeeper” – a barrier to entry – and recommending rewriting it for clarity and inclusivity. The second statement received a much lower score of 8.16 (compared to 30.36 for the first, which is comparable to postgraduate research papers in terms of language complexity), confirming that it is suitable for attracting entry-level candidates.

# wrapping up

The job description is often the front door to a company, and excessive business jargon can act as a bouncer in situations where openness matters most – especially for entry-level roles. This article shows how to use TextStat’s Gunning Fog Index to create a simple, automated text auditor that identifies overly complex job descriptions, helping to ensure clear, direct, and accessible language that keeps your job listings open to every entry-level talent.

ivan palomares carrascosa Is a leader, author, speaker and consultant in AI, Machine Learning, Deep Learning and LLM. He trains and guides others in using AI in the real world.