A Crowded Future: Working against Abstraction on Turker Nation
Bibliographic record
Abstract
This paper examines digital labor and community through an ethnography of a discussion board supporting short-term digital contract workers on the Amazon Mechanical Turk (mTurk). First, we give a thorough overview of mTurk, the crowdsourcing marketplace, and Turker Nation, a discussion board for workers on mTurk. We trace the experience of interacting with this infrastructure on mTurk as worker and employer. Following, we look at scholarship on software infrastructure and autonomous Marxist theorizations of contemporary work. We demonstrate how the labor of participating on the discussion board Turker Nation helps to counter the abstraction the infrastructure provides. We show how workers on Turker Nation use the platform to structure time, build socializing spaces at work and initiate collective organizing. In doing so, we argue that workers’ labor belies conventional class classification, such as white-collar and blue-collar labor and instead lays the groundwork for how to structure future digital workplaces. We argue that this laboring resists the assumed logic of capitalism for digital labor that subsumes and takes over workers’ lives and conclude by looking at the limitations of the community’s collective organizing in terms of agreeing on points to communicated to the public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.035 | 0.033 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".