“Do I Look Like My Selfie?”: Filters and the Digital-Forensic Gaze
Bibliographic record
Abstract
Filtered faces are some of the most heavily engaged photos on social media. The vast majority of literature on selfies have focused on self-reported practices of creating and posting selfies and how subjects view themselves, but research on using filters and the kinds of looking filter provoke is underexplored. Part of a larger project, this analysis draws from a study using photo-elicitation techniques to discuss selfie filters with 12 focus groups, exploring the dominant discourses of cis-gendered looking within digital sociality. We explore how participants edit their selfies, imagine potential audiences, interact with, and perceive the filtering behaviors of others, asking what the “work” of filters is, visually and socially. We probe the kinds of discourses filters participate in, and their gendered and affective dimensions. Our focus groups indicate that when looking at the selfies of others there is often an a priori assumption that filtering has been applied, whether conspicuously or not, to the extent that visual tune-ups have become central to the genre itself. As such, we explore the ambivalence and anxiety about authenticity that filters produce, as well as the intense looking practices aimed at decoding the legitimacy of images. We posit that filters are part of a digital ecosystem that demands an intensification of looking practices, which produce and enhance specific forms of objectification directed toward selves and others within digital environments.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".