Disinformation-for-Hire as Everyday Digital Labor: Introduction to the Special Issue
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This introduction for the special issue “Disinformation-for-Hire as Everyday Digital Labor” carves out a specific area of inquiry within the ever-growing field of disinformation studies, with its sharp focus on the commercial transactions, organizational logics, and entrepreneurial practices that propel the production of disinformation. Inspired by traditions of political economy, media production studies, and everyday life approaches, this framework draws analytical focus to (1) the slow-burn horror of disinformation as everyday digital labor; (2) the diverse industries and workers engaged in disinformation production; and (3) regulatory areas beyond social media content policy and platform-centric accountability—especially relevant in the Global Majority context. Furthermore, this essay discusses how digital labor studies need to engage more directly with the ways disinformation thrives in the gray in-betweens of formal/informal and licit/illicit digital economies. The essays in our collection mobilize “disinformation-for-hire” as a valuable analytical frame that lays bare disinformation as a product of commercial and political complicities in the late capitalist arrangement of transnational digital industries.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it