John Gallagher

Associate Professor

University of Illinois System

United States

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About

John Gallagher is an Associate Professor at the University of Illinois System in the United States. His research focuses on the intersection of artificial intelligence, technical communication, and writing studies, as demonstrated in his recent articles. His work includes analyzing algorithmic behaviors, exploring effective communication practices in AI and machine learning, and examining trends in writing studies journals. Gallagher's contributions aim to illuminate challenges in technical writing and enhance pedagogical approaches within STEM disciplines.

Articles (13)

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2025

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2025

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2025

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2023

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2023

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2023

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2023

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2022

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2020

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2020

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2020

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2020

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2020

Collaborators (1)

Paul Prior

Professor

University of Illinois at Urbana-Champaign

UNITED STATES

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John Gallagher

Research Interests

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Articles (13)

When collaborating turns into dishonesty: A data-driven heuristic comparing human and AI collaborators

Algorithmic Anthropomorphizing, Platform Gossip, and Backlashes: Aspirational Content Creators’ Narratives About YouTube’s Algorithm on Reddit

The challenges and writing practices of communicating artificial intelligence and machine learning in an era of hype

Emojination Facilitates Inclusive Emoji Design Through Technical Writing: Fitting Tactical Technical Communication Inside Institutional Structures

Analyses of seven writing studies journals, 2000–2019, Part I: Statistical trends in references cited and lexical diversity

Analyses of seven writing studies journals, 2000–2019, Part II: Data-driven identification of keywords

Required Templates: An Assemblage Theory Analysis of How Template Character Limits Influence the Writing of DIY Online Grant Proposals

Project-Oriented Web Scraping in Technical Communication Research

Rhetorical Appeals and Tactics in New York Times Comments About Vaccines: Qualitative Analysis

A Collaborative Longitudinal Design for Supporting Writing Pedagogies of STEM Faculty

Corrigendum: “Writing for Algorithmic Audiences” (Computers and Composition (2017) 45 (25–35), (S8755461516300652), (10.1016/j.compcom.2017.06.002))

Machine Time: Unifying Chronos and Kairos in an Era of Ubiquitous Technologies

Peering into the Internet Abyss: Using Big Data Audience Analysis to Understand Online Comments

Collaborators (1)

Paul Prior

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