Bibliographic record
Abstract
This article leverages the work of Karen Barad to analyze digital self-imaging research. Drawing on findings from four interviews with avid selfie authors, this article argues that agential realism can provide a rich ontological framework for examining selfies that goes beyond the representational paradigm in some studies of socially mediated digital images. Rather than beginning the study with the presumption that bodies, photos, cameras, and expressed selves are distinct and pre-existing entities that then interact with one another, or touch, selfies here are construed as networked material–discursive entanglements wherein bodies, photos, cameras, and expressed selves are always and already touching. Within this entangled phenomenon, then, this article suggests that what reads as touch (images that grab or repulse/efface) is in a sense the opposite of touch—it is a pulling apart of the entangled phenomenon wherein agential cuts demarcate the desired boundaries of entities like bodies, images, and self. This article further suggests that what makes and doesn’t make the “cut” is not natural but emerges within gendered apparatuses of bodily production.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".