Live Hypernarrative and Cybercartography: You Are Here, Now
Bibliographic record
Abstract
This article explores some of the potentialities of narration in the context of cybercartography. We have developed a new kind of dynamic or “live” form of hypernarrative, in which the content and structure of stories is determined by live information. This system would ultimately allow the creation of hypermedia narratives capable of mining public databases on the fly in order to customize and integrate narrative material appropriate to the user's particular temporal and geospatial context. Unlike other forms of hypermedia, a live hypertext narrative can actually be different every time it is read. More akin to an improvised performance than to a recorded one, a live hypertext changes depending on where and when it is accessed, and on what is happening in the world and on the Web. Live hypertext thus presents a new development in the history of writing that challenges our inherited notions of the stability, fixity, and even authority of printed text. The role of live data and the spatial and temporal aspects of the data suggest strong connections to cybercartographic environments. Not only are the same data sets relevant to both hypernarrative and cybercartography, but the nature of the hypernarrative shows new possibilities for cartographic environments. In particular, narrative and end-user navigation in a story show new ways of involving users, a key principle of cybercartography.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".