Bibliographic record
Abstract
Future collections are redefined and curated as immaterial fictions, echoes of materialistic dwellings and synthesized human recordings. As individuals we will no longer be occupied with the act of archiving, but rather these future curations will be preserving humanity. Our initial cabinets of curiosities have disappeared and there is no longer any delay in our sense of gratification. By Inverting the narrative and composition of human data, other truths or errors are exposed. Imperfections are not seen as failure, but as a balance between harmony in a composition and an ever-improving technique. In order to investigate the immateriality of collections, this thesis narrates and conducts a speculative study on the evolution of an artificially autonomous voice, named KEYA. KEYA is conducting an investigation on the Pantheon. The Pantheon is a trope and is the merging point between two other random searches. Merging locations such as the Persepolis and the Seed Vault. What she narrates will be one edition of her curation and has multiple editions of the same search conducting a new narrative a the same time. KEYA is one of many and this thesis is highlighting only 1 second of her search and evolution that breaks through the physical and exposes the immaterial.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".