Outside the margins and beyond the page: complex digital literature, the new horizon for acquisition, conservation, curation and research
Bibliographic record
Abstract
Some born-digital literary works are not simply large, static electronic files. Termed “complex digital literature,” these technology-dependant literary works require the author to adapt or re-engineer hardware or software as part of achieving an aesthetic. This, in turn, makes the works challenging for archives to acquire, preserve and make available to researchers. The article provides a brief history of the pioneers and evolution of digital literature. Informed by the lessons learned from past experiences with born-digital records, it also considers the archivist’s role in dealing with present and future complex digital literature – a role that requires balancing the familiar duties of acquiring, preserving and providing access to records with the delicate task of conveying their interactive nature and experiential facets. Much like authors of complex digital literary works who are drawing influences from different artistic disciplines, archivists can look beyond the archival community’s response to the digital era. The article concludes with a discussion of how professionals in complementary visual art and museology fields are pursuing the acquisition of art forms that rely heavily on innovative technology and what archivists might learn from their preservation-oriented approach.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.041 |
| Scholarly communication | 0.032 | 0.035 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".