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.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.037 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".