The 'Entry Level' Gatekeeper: Examining Job Descriptions with Textstat

# Introduction

Have you ever come across an “entry-level” job description where the candidate's requirements include intangibles such as “leveraging cross-functional paradigms to develop synergistic results”, or worse? When HR documents are full of jargon or corporate jargon, they not only confuse readers but also intimidate talented, talented job seekers. Since the first step to inclusiveness is accessibility, why not ensure that your job descriptions maintain an accessible tone through research processes?

This article shows how to use free, open source tools like Python and it Textstat a natural language processing (NLP) library to create a script that automates the process of capturing “gatekeeping language” from job descriptions before publishing them.

# Key Ingredient: Shooting Fog Indicator

I Gunning Fog Index — available on Textstat by using textstat.gunning_fog – it's an excellent way to test text, especially for entry-level job listings. In practice, this index can be used to estimate the number of years of formal education a person might need to understand a text when they first read.

Its calculation is based on the observation of two main factors: the average sentence length and the percentage of complex terms – usually words with three syllables or more. Note that business jargon often abuses multi-syllable words such as “operations”, “methods”, and so on. Therefore, the Gunning Fog Index comes very close to our goal of checking job descriptions to ensure they are not overly complex for the target profile they are intended to attract. In other words, it helps ensure that the language is clear and accessible. A low value for this index means greater clarity and accessibility.

# Examining an Example with Textstat

An important first step is to install the Textstat library for Python if you haven't already done so:

The core idea of ​​our script will reside in a reusable function whose purpose is to evaluate input text – eg an entry-level function definition:

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 exercise are very simple. First, we get straight to the point and calculate the Gunning Fog score of the text (probably a job description) passed as an entry. This school, maintained at fog_scoreit goes through a simple context-based check to produce three different decisions based on text complexity — it's like a three-color traffic light system.

In general, a text with a Gunning Fog score below 10 is considered accessible and suitable 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 major revision.

Next, it's time to test our auditor by passing him two different 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 an excellent job of recognizing the original definition as a clear “gatekeeper” – a barrier to entry – and recommended that it be rewritten to be clearer and more inclusive. The second definition received a very low score of 8.16 (compared to 30.36 for the first, which is compared to postgraduate research papers in terms of language difficulty), which ensures that it is well suited to attract candidates.

# Wrapping up

Job descriptions are often a company's front door, and excessive business jargon can act as a bouncer in situations where openings are most important – especially entry-level roles. This article showed how to use Textstat's Gunning Fog Index to create a simple, automated text auditor that identifies the most complex job descriptions, helping to ensure clear, concise, and accessible language that keeps your job listings open to all entry-level talent.

Iván Palomares Carrascosa is a leader, author, speaker, and consultant in AI, machine learning, deep learning and LLMs. He trains and guides others in using AI in the real world.