Following Mechanical Turks
Articulating the Human in "Human Intelligence Tasks"
Most Recent Episode:
Regular video discussions every other Friday. Next video discussion May 20 at 9:00 a.m. Eastern.
This Enculturation: Intermezzo research project analyzes Amazon Mechanical Turk (MTurk) job posts—called Human Intelligence Tasks or HITs—to trace how they rhetorically depict human actants.
The project is an experiment in open, ad hoc scholarly collaboration among multiple humans and non-humans. It is unfolding through regularly scheduled public streaming video sessions as the principal investigators (Jeremy Tirrell and Nathaniel Rivers) move from initial concept to completed text. Anyone can participate in these discussions, and several of them include invited guests as the work uncovers issues that would benefit from particular expertise. Video sessions and associated materials—textual exchanges, research documents, relevant links, etc.—are archived here and publicly shared. Manuscript drafts are available online during development and are open to in-progress annotations, with the hope that they will formatively shape a paper's direction, rather than function as post factum comments on a stable text.
Would you like to participate? Join the live streaming video sessions, comment on the manuscript draft, or contact Jeremy and Nathaniel.
Texts
Meta-documents
Project Overview
by Dr. Jeremy Tirrell, Dr. Nathaniel RiversProject Credits
by Dr. Jeremy Tirrell, Dr. Nathaniel Rivers
Research Products
Tracing the Human in Amazon Mechanical Turk through Rhetorical Text Mining
by Dr. Jeremy Tirrell, Dr. Nathaniel RiversThis study uses text mining to examine how the language of posts on Amazon’s Mechanical Turk (MTurk), an online global crowdsource marketplace, rhetorically articulates a model of the human that emerges from concrete labor acts rather than abstract moral or ethical capacities. The study analyzes 23,105 documents using multiple operations including weighted frequency counts, clusters and topic models, and frequency co-occurrence and n-gram diagrams at three grain sizes: the whole corpus, posts organized into five tiers based on compensation level, and specific term associations. Findings identify coherent human practices that collapse bodily and intellectual capabilities, but a uniform distinction between humans and non-humans is unresolved, because MTurk posts repeatedly redraw such boundaries, causing characteristic traits to be variably shared.
Drafts
Surveying Rhetorical Readability on Amazon Mechanical Turk
by Dr. Jeremy Tirrell, Dr. Nathaniel RiversThis is the working draft of the second research project
Resources
Resource Collections
Single Resources
Image A 1980s reconstruction of the original Mechanical Turk
Pdf A sample compensation keyness diagrams
Image Bigram network graph unweighted
Image Bigram network graph weighted
Video Computers and Composition Conference Talk (6-22-19)
Image Cookie Theft Picture
Video Dr. Casey Boyle on distinctions emerging from interactions
Video Dr. Casey Boyle on the name “Mechanical Turk”
Video Dr. Casey Boyle on the variable roles for humans and non-humans when producing readability
Video Dr. James. J. Brown, Jr. on labor realignments in MTurk