“Entry Level” Gatekeeper: Checking Job Descriptions with Textstat

# Entry

Have you ever encountered an “entry-level” job description where the candidate requirements include inscrutable aspects such as “using cross-functional paradigms to optimize synergies” or even worse? When HR documents are full of dense jargon or business terms, they not only confuse readers, but also discourage talented, capable job candidates. Since the first step towards inclusion is accessibility, why not ensure that job descriptions maintain an accessible tone within audit processes?

This article shows you how to exploit free, open source tools such as Python and its Text statistics a natural language processing (NLP) library to build a script that automates the process of capturing the “guard language” in job descriptions before their publication.

# Key Ingredient: Shooting Fog Indicator

The Shooting Fog Index – available in Textstat using textstat.gunning_fog — this is an excellent approach to audit text, especially entry-level job listings. Essentially, this metric can be used to estimate the number of years of formal education a person may need to understand a text the first time.

Its calculations are based on observations of two main factors: average sentence length and the percentage of compound terms—usually words with three or more syllables. Be aware that business jargon often overuses multi-syllabic buzzwords such as “operationalization,” “methodologies,” and so on. Therefore, the Gunning Fog Index comes very close to its intended purpose of auditing job descriptions to ensure that they are not too convoluted for the intended profile they are intended to attract. In other words, it helps ensure that the language is clear and accessible. A lower value of this indicator means greater transparency and accessibility.

# Audit the example with Textstat

The first key step is to install the Textstat library for Python if you haven’t already done so:

The basic logic of our script will be a reusable function whose purpose is to control the input text – e.g. 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 

The steps in the previous function are quite uncomplicated. First, we get straight to the point and calculate the Gunning Fog score for the text (probably a job description) passed as input. This result, recorded in fog_scoreundergoes a uncomplicated condition-based check to generate three different verdicts based on text complexity – similar to a three-color traffic lightweight 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 convoluted, and a score above 14 is considered highly convoluted and requires significant revision.

Then it’s time to test our auditor by giving him two different sample 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 lend a hand us grow our marketing channels.
You will work closely with different teams to launch campaigns, track how well they do, and find fresh ways to improve.
Your goal is to lend a hand 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))

Exit:

--- 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 recognizing the first description as a clear “gatekeeper” – a barrier to entry – and recommended rewriting it to ensure clarity and inclusion. The second brief received a significantly lower rating of 8.16 (compared to 30.36 for the first, which is comparable to postgraduate research papers in terms of linguistic complexity), confirming that it is well suited to attracting entry-level candidates.

# Summary

Job descriptions are often the calling card of a company, and excessive business jargon can act as a distraction in situations where openness is most significant – especially for entry-level positions. This article shows you how to exploit Textstat’s Gunning Fog Index to build a uncomplicated, automated text auditor that identifies overly convoluted job descriptions, helping you provide clear, direct, and accessible language so your job openings are open to any entry-level talent.

Ivan Palomares Carrascosa is a thought leader, writer, speaker and advisor in the fields of Artificial Intelligence, Machine Learning, Deep Learning and LLM. Trains and advises others on the exploit of artificial intelligence in the real world.