Reading the cuts : a novel postman digital visual methodology for examining images of the self on social media
Bibliographic record
Abstract
This dissertation presents a novel posthuman digital visual methodology for studying digital self-imaging practices on social media. The method considers digital images, the images they produce, and the audiences to whom the images are shared not as distinct entities but rather as entangles assemblages of material, discursive, and affective forces that intra-act together to create digital imaging phenomena. Reading the Cuts draws on the work of Karen Barad, Don Ihde, and Gayle Salamon as the foundation of the methodology. The dissertation provides an overview of literature written about selfies or digital self-images shared on social media. It then provides a posthuman narrative of the becoming of the paradigms that have come to shape how we think about the relationship between digital images and digital subjectivities. The dissertation then narrates how the Reading the Cuts came to be, theoretically, and positions itself as contributing to both classic qualitative visual methods and post qualitative methodologies. Reading the Cuts as a methodology aims to provide a posthuman approach to visual methods that challenges typical representational modes of analyzing images in social media spaces by studying the becoming of digital self-images.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".