Crowd Work on a CV? Understanding How AMT Fits into Turkers' Career Goals and Professional Profiles
February 13, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Anna Kasunic, Chun-Wei Chiang, Geoff Kaufman, Saiph Savage
arXiv ID
1902.05361
Category
cs.HC: Human-Computer Interaction
Citations
7
Venue
arXiv.org
Last Checked
4 months ago
Abstract
In 2013, scholars laid out a framework for a sustainable, ethical future of crowd work, recommending career ladders so that crowd work can lead to career advancement and more economic mobility. Five years later, we consider this vision in the context of Amazon Mechanical Turk (AMT). To understand how workers currently view their experiences on AMT, and how they publicly present and share these experiences in their professional lives, we conducted a survey study with workers on AMT (n=98). The survey we administered included a combination of multiple choice, binary, and open-ended (short paragraph) items gauging Turkers' perceptions of their experiences on AMT within the context of their broader work experience and career goals. This work extends existing understandings of who crowd workers are and why they crowd work by seeking to better understand how crowd work factors into Turkers' professional profiles, and how we can subsequently better support crowd workers in their career advancement. Our survey results can inform the design of better tools to empower crowd workers in their professional development both inside and outside of AMT.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted