The Politics of Gray Data: Digital Methods, Intimate Proximity, and Research Ethics for Work on the “Alt-Right”
Bibliographic record
Abstract
This article addresses how gray data, or research data that have their provenance in the gray area between found texts and the products of participants, is complicated by issues inherent to studying the “alt-right,” especially in social justice–oriented and digital methods work. Although ethical guidelines and recommendations have not reached a consensus on issues such as requiring consent for doing work on gray data in general, fruitful contextual discussions that take in differing worldviews and political goals can help triangulate an approach to making decisions for specific projects. Furthermore, the overt hostility of “alt-right” groups to researchers is also considered as a complicating factor, one that extends the meaning of “ethical responsibility” to also include responsibilities to additional parties, such as those you are citing, research assistants, and family members. The article concludes with a consideration of the intimate proximities created by social justice–oriented and digital methods research on the “alt-right,” and a set of guiding questions for doing such work that, while not quite a set of best practices, are offered as signposts to help researchers navigate what are ultimately highly singular and emergent ethical problematics.
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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 | 0.363 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.028 | 0.248 |
| Scholarly communication | 0.038 | 0.061 |
| Open science | 0.005 | 0.040 |
| Research integrity | 0.013 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 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".