The Electronic Representation of Information: New Relationships between the Virtual Archive and its (Possible) Referent
Bibliographic record
Abstract
“Our life is half natural and half technological. Half-and-half is good. You cannot deny that high-tech is progress. We need it for jobs. Yet if you make only high-tech, you make war. So we must have a strong human element to keep modesty and natural life.” ”” Nam June Paik [1] The present work focuses on the new relationship generated by electronic information between the virtual archive (the Web in a broad sense, certain specialized archives in particular) and its referent (material reality in general, museums, inter-art practices, and artworks in particular). It proposes that the relationship between information, its representation and the referent (or in other words, the relation between reality and the conceptual construction of reality) has to be re-thought. Douglas C. McGill, “Art People,” The New York Times , October 3, 1986, http://www.nytimes.com/1986/10/03/arts/art-people.html (accessed September 1, 2013).
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.028 | 0.052 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".