Disinformation-for-Hire as Everyday Digital Labor: Introduction to the Special Issue
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.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".