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
This brief experimental text explores metaphors of recognition in a computational poetics of generative AI imagery. Artificial intelligence (AI) is framed as a transitional entity initializing a journey through a latent space of algorithmic self-reflection, mediating the emergent polarities of chaos and cosmos. The conflation of text and image engages with the conflict between linear temporality and speculative futurism that creative process attempts to bring into alignment in the epistemology of composition. The image informs the text, and the text informs the image in an iterative cycle of anticipation and reflection. Employing the myth of the Centaur and its resonance with AI image development, the text questions the legitimacy of boundary schema in latent space. The hybrid beast-human offers an origins story of possible futures of the manifest image poised at the interstice of analog human and digital machine intention, where (algorithmic) abstraction turns imagination to representation and representation determines what humans may become under the recursive watch of AI. Drawing from poet C. P. Cavafy and conceptual metaphor theory this postphenomenological intervention aims to expose alignments between pre-technological mythology and posthuman mythocracy in a narrative trace through the subjective madness of pareidolic familiarity in the age of technic imagination
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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