Bibliographic record
Abstract
Anonymous social media platforms comprise sprawling publics where users, untethered from their legal identity, interact with other anonymous users to share and read user-generated content. The emergence of mobile applications that promise anonymity as a primary feature has led to novel social configurations that have so far been understudied. This article is an empirical and theoretical attempt to understand the ways in which anonymous social media have altered the social through performative, digital acts mediated through a community of anonymous users. I do this through two main propositions: first, that practices of anonymity mediated through a social media platform comprise discrete performative acts of identity due to a process of dissociability, and second, that those dissociated performative acts become undisciplined. To address these propositions, I have drawn from 12 semi-structured interviews of undergraduate and graduate students at Queen’s University who were avid users of Yik Yak to explore the sociology of anonymity, surveillance, and identity. The findings are discussed in relation to theories of performativity and discipline that have been commonly deployed in media and surveillance studies.
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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.132 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".