The Politics of Gray Data: Digital Methods, Intimate Proximity, and Research Ethics for Work on the “Alt-Right”
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 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.
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 | 0.035 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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