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
In this paper, I have sought to introduce and outline the trend in Internet meme-making know as 'deep-frying' and explain its significance as a method of user critique within a naturalized medium. How do images that confuse and repel the casual viewer through profanity, enthusiastic emoji usage, over-saturation, repeated compression, bubbling/ warping, and excessive lens flaring effectively question the memetic paradigm? Firstly, by understanding memes as Hito Steryl's transgressive 'poor images' that circulate to produce communities of content creators and consumers that stand in opposition to the state-sponsored rich image making complex. Further, through the application of work by Rolande Barthes, Claude Shannon & Warren Weaver, Scott Contreras-Kotterbay & Łukasz Mirocha, and Rosa Menkman, I have produced a critical examination of the formal practices that elucidate this phenomenon, on the level of the linguistic & iconic message, noise level, and redundancy. Lastly, I propose an orientation of these works within a diverse corpus across various major social media as a networked art practice in keeping with the tenants of the New Aesthetic.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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